Marcus Lewin
email: lewinm@hsu-hh.de

Helmut Weber
email: weberh@hsu-hh.de

Alexander Fay
email: alexander.fay@hsu-hh.de

Helmut Schmidt

Optimization of Production-Oriented Logistics Processes Through Camera-Based Identification and Localization for Cyber-Physical Systems

Keywords: camera, camera-based sensor, combining data, logistics, Cyberphysical Systems, optimization, visualization, Industry 4.0

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Increasing customer requirements and individualized products lead to a growth of complexity in production processes and the amount logistics processes due to distributed production sites and higher complex delivery networks [START_REF] Bauernhansl | Die Vierte Industrielle Revolution -Der Weg in ein wertschaffendes Produktionsparadigma[END_REF]. To meet these higher requirements, a new level of organization and control of the entire value chain over the lifecycle of products is necessary [START_REF] Dorst | Implementation Strategy Industrie 4.0. Report on the results of the Industrie 4.0 Platform[END_REF]. This is summarized under the term Industry 4.0 based on Cyber-Physical Systems (CPS).

CPS are embedded open-ended socio-technical systems and enable a range of new functions, services and features for production, logistics, engineering, coordination and management processes as well as internet services [START_REF] Kagermann | Recommendations for Implementing the strategic initiative INDUSTRIE 4.0: securing the future of German manufacturing industry[END_REF]. An essential basis is the availability of all relevant information in real time through directly captured physical data by sensors and networking of entities involved in the value creation as well as the ability to derive optimal value-added flows from data obtained [START_REF] Dorst | Implementation Strategy Industrie 4.0. Report on the results of the Industrie 4.0 Platform[END_REF]. Industry 4.0 contains vertical integration and networking of production systems [START_REF] Adolphs | Status Report-Reference Architecture Model Industrie 4.0[END_REF]. These aspects include the areas of sensor networks as well as intelligence, flexibility and changeability of systems through comprehensive solutions. In particular, sensor networks provide a new way of process understanding by permanently collecting and analyzing sensor data from production and logistics. [START_REF] Bauernhansl | Industrie 4.0-Whitepaper FuE-Themen[END_REF] This includes further development of camera-based sensor technology to intelligent, configurable and networked sensor systems with case-specific and adaptive measurement strategies [START_REF] Bischoff | Erschließen der Potenziale der Anwendung von Industrie 4.0 im Mittelstand. agiplan GmbH[END_REF]. After an overview of related work, we show an approach using camera-based sensors in combination with joined data from different sources, data analysis and visualizations to analyze and control CPS to improve production-oriented logistics. An implementation in a modular production system and opportunities of the approach are discussed subsequently.

Developments in sensor technology and related work

Various methods for obtaining production and material flow data, such as manual data collection by employees, applications of auto-ID techniques (bar code or RFID techniques), subsequent analysis of film recordings, analysis of production documents and statistics or data collection from ERP systems are available [START_REF] Bohács | Automatische visuelle Datensammlung aus Materialflusssystemen und ihre Anwendung in Simulationsmodellen[END_REF]. Through the continuous development of sensor technologies, new and more effective methods for obtaining these data could be developed [START_REF] Richter | Logistiksysteme[END_REF].

Camera-based data collection systems can provide a useful complement to existing data collection methods. This increases the availability of distributed information from a wide range of sources, which also have to be analyzed in real time and can be used for system control. Providing information for self-control becomes increasingly important. New camera models for camera-based systems have the capability to build the function of different sensors and to generate virtual sensor data in the desired quality. This allows an analysis of the effects for sensor-specific and process-specific parameters on situation analysis [START_REF]Nationaler IT-Gipfel Berlin 2015 Plattform "Digitale Netze und Mobilität[END_REF].

Concerning the use of camera-based sensors in logistics, the number of applications and research projects is limited [START_REF] Özgür | Comparing sensor-based and camera-based approaches to recognizing the occupancy status of the load handling device of forklift trucks[END_REF][START_REF] Borstell | Bildbasierte Situationsanalyse unterstützt Logistikprozesse[END_REF]. Computer vision is used as a method for realizing and replacing different sensors on forklifts trucks with just one technology at the project "Staplerauge" [START_REF] Jung | Staplerauge": A Framework for Camera-Based Sensor Functions on Forklift Trucks[END_REF]. The replacement or supplementation for various sensors such as localization of hoist height determination and fork-occupied detection leads to decreasing integration costs on forklifts trucks and background IT-systems. In practice the technical feasibility and a high potential of the vision-based state monitoring for forklifts has been proved [START_REF] Hohenstein | Das Staplerauge" -Vision und Wirklichkeit[END_REF].

A different approach pursue the marker-based location of resources at the project MarLO [START_REF] Richter | Logistiksysteme[END_REF]. Fixed infrastructure is equipped with appropriate video sensors to detect optical markers on different mobile logistics resources like forklift trucks. Marked objects are tracked from the outside and the acquisition and calculation systems are firmly connected with the sensor processing units. This system is advantageous for spatially restricted application environments as well as for free surfaces, in which numerous mobile operating means are used.

At the project tracing intelligent logistics objects (TiLo) the feasibility of data acquisition and analysis by the industrial image processing and the use of simulation studies are demonstrated [START_REF] Koç | Nutzung von Realdaten in Simulationsmodellen durch industrielle Bildverarbeitung[END_REF]. The used camera covers only individual areas and the obtained data is only used for simulations. There is also no connection of the camera data with production and sensor data.

The presented projects often show only individual elements of Industry 4.0. A comprehensive connection of camera-based data with further production data analysis and evaluation in combination with visualization in real time and for simulation is not achieved in the production environment.

Technical concept

In addition to the necessary basic conditions, the technical concept includes four stages. First, the type of identifiers as well as the type of identification and localization methods are defined. An interface to existing production databases is created subsequently. In a third step the camera-based data and the data from the existing systems have to be prepared and evaluated in order to make decisions and to support workers through appropriate visualizations in a final step. Such virtual sensors are a step ahead to reach Industry 4.0 goals. Figure 1 shows the complete process.

Requirements and necessary conditions

The use of camera-based sensors needs various minimum requirements and conditions, which must be taken in order to achieve a secure identification and localization of objects in a required quality. For locating different equipment by camera-based systems, two essential technical approaches are possible. Either the mobile object itself captures its surroundings. For this purpose, cameras are installed directly on the objects and markers are placed firmly in the environment. Or the mobile object is captured, realized by cameras attached in the infrastructure and markers mounted the objects. The size of the covered area is to be taken into consideration. Depending on the design, several camera-based sensors have to be used for larger areas. This leads to an increase in system complexity. Furthermore, the type of the relevant objects have to be taken into account. In addition to natural identifiers, artificial identifiers can be used too. The size of the objects observed as well as the distance leads to minimum requirements for the resolution of camera-based sensors. Influence on the minimum sampling rate of the sensors has among other things the speed of the objects. The light conditions on the spot have an influence on the quality of the object detection. These factors influence the exact design of the camera-based sensors.

Image Processing and Localization Algorithm

Because artificial identifiers often lead to a more stable detection of objects, further matrix codes are discussed. ArUco markers are synthetic square marker composed by a black border and an inner binary matrix containing a number as an ID. The ID and the direction and size of these markers is easy to detect with software libraries to analyze camera images, like the open source computer vision OpenCV. For the recognition of the markers, they need at least the space of about 30x30 pixels of the camera image.

The resulting data of the recognition -ID, Coordinates of the edges -are used to compute more derived information: direction and speed of the objects, distances to other objects etc. These data may be seen as received from virtual sensors and are saved for further investigation. The object itself does not need any sensors or wireless communication facilities, but if it is able to communicate, it could be provided with all these data concerning itself or all other marked objects. The data seems to come from a superset of sensor built in -virtual sensors. To reach the goals of Industry 4.0, these data may be distributed to the different elements of the production in real time.

Connection and processing of data from different sources

A software interface to relevant production database has to be implemented to read the state of all involved elements to combine these data with the data of marker recognition and derived data together with the timestamp to build a database of the production process containing all relevant information. This data could be used to monitor the production process just in time or later in simulations. Chapter 5 discusses these possibilities.

Visualization and worker support

These compacted data from different sources are used to visualize the production process in a different view like augmented vision or a virtual picture of a system. The augmented vision combines the real camera image with additional superimpositions. Thus provides a detailed insight into systems along with necessary added information. The camera images are not necessary needed any longer for the virtual view -may be stored for some time for controlling purposes -and the compact sensor may be transferred to any place to see the production in real time or later. The commented and with all information added data can be used to monitor and control the production. Based on this added data new decisions could be made to optimize production processes.

Demonstrator and implementation on an CPS

The presented concept is implemented in a modular production system shown in figure 2. This CPS produces individualized valves in small batches up to lot size 1. A work piece carrier equipped with a RFID tag contains orders, work plans and work pieces.

The production line consists of six connection points for various production modules and a conveyor belt circuit. To realize an improved material flow control, the system has two switch points to skip stations. Each module is decentral controlled via its own PLC. Orders and certain production steps are also documented centrally in a MES with appropriate databases. In the first module, three different basic bodies are provided from stock. The second module performs several machining operations, such as drilling. Quality control is performed in the third module. A robot assembles individual components of a valve in the fourth module, which can subsequently be stored in the fifth module. Module six commissions and deliveries finished orders to customers. Based on this system, various hardware changes were performed based on the process shown in Figure 1. This includes in a first step the placement of ArUco markers on the work piece carriers for identification and localization. A camera was installed centrally above the modular production system and provides images of the whole system. The analysis software, located on an external PC, recognizes the parameters of all markers inside the image and continuously calculates the position of all carriers almost in real time, shown in figure 2. The parameters of the markers (ID, position of the center and the position of the corners of the markers) are stored in a database table together with time stamps. The alignment, the speed and the direction of movement of the markers can be calculated from these image sequences. This design of the camera system is easy to implement with minimal interferences of the production and logistics processes.

Fig. 2. Augmented camera picture

Secondly, the production databases of the MES were connected to the software through a two-way interface. During the data connection and analysis, the tables of the production database are constantly read out. These data are combined with the data from the position calculation of the markers. According to this, in a third step the system derives different evaluations and results. In the example shown in figure 3, eight carriers are used. Two carriers (3 and 5) being filled with a basic cylinder. Carrier 3 moves to the station "Camera" for a quality check. Carrier 5 transported its basic cylinder to the station "Driller" and waits for the completion of the drilling process.

The detailed view of production and logistics processes leads to abilities for selfoptimization, self-configuration and self-diagnosis, which are essential for Industry 4.0. Standstills of the conveyor belt can be detected immediately. The blockage of the belt by a jammed carrier also between the stations triggers an alarm message. In addition, the evaluation determines the service life of a carrier at a station, whether the processes run stably or require more time than usual. The position of the individual carriers is determined continuously and independently from sensors of the individual modules.

This allows a more precise localization of the carriers and a higher transparency of logistics processes. The position and the type of parts on the carriers can also be determined precisely. The software identifies the markers of the loaded parts and evaluates the images in combination with the data from the MES. Thereby a plausibility check of the work pieces (presence, color and diameter) is possible. Different lengths of processing steps on individual modules can cause queues, which cannot be adequately regulated by the decentralized control, without an image of the entire system.

In addition to a real time evaluation, the data can be used for later simulations too. Without intervention in the plant, the influence of different parameters and strategies on the production can be tested. This includes changes in the belt speed, changes in the duration of individual production steps or influence of the distance of the stations. Layout decisions and optimizations can also be determined.

Fig. 3. Virtual view of the system with camera-based identification and analytics

Several views are implemented for the visualization of the system within the fourth process step. One view shows the camera image of the plant with augmented information of identified carriers and important parameters from the production database. Figure 3 shows the virtualized representation of the plant with the current status of the orders, work pieces and work piece carriers. This view gives a good overview of the production and logistics processes including messages and warnings.

Optimization methods and opportunities of the approach

The presented concept leads to a shift of hardware functions to software functions via software modules. These software modules are the basis for higher flexibility and changeability for CPS and in production-oriented logistics.

The presented camera-based systems have the advantage that they do not need a direct connection to the existing controls of the systems. Therefore, they collect information independently from existing automation and control data of production plants. Changes of the camera and evaluation software can be made during production. This leads to minimal disturbances in production and logistics. In this way, camera-based systems can collect data on details that cannot be captured or can only be captured at great expense by installed sensors in existing production facilities.

The continuous connection of identification and localization with different production data can be used to monitor a production. Already in a simple structure, an augmented representation of the current production is possible over the network or internet. Standstills of transport systems, such as conveyor belts, can be detected immediately and an alarm message can be triggered. Processing and handling times of production stations can be determined and evaluated, too. Without direct encroachment on plants, data for simulations can be collected. The data can be used to test the influence of different parameters and strategies in production and logistics. Thus leads to an evaluation and optimization of individual production stations, identification of bottlenecks and to improved layout decision for production networks. In addition to the presented possibilities, there are numerous other possibilities for optimization for CPS in Industry 4.0:

• Providing high-resolution state data and relevant information to entities and systems in real time through directly captured physical data and data analysis for an adaptive production control • Plausibility comparison with the signals from other sensors, e.g. combination of artificial with natural identification features or production data • Replacement and extension of various existing sensors • Continuous monitoring and Real time control of processes • Increasing the transparency and system understanding of the production and logistics facilities by recording and analyzing services and loads • Support for workers through clear visualization and decision-making • Later made simulations based on the data could help to discover and eliminate weaknesses and errors and to optimize production

Conclusion and Outlook

In this paper, various possibilities for the use of camera-based sensor data in production and logistics were presented. Camera-based sensor technologies become more effective, more powerful and more cost-effective, and will be used largely in logistics and production systems. By combining the resolved sensor data with other data from the production and logistics, CPS control can be improved and optimized. The presented methods of visualization provide a good basis for system monitoring and decision support of workers. Therefore, combining of different data resources leads to an improvement and optimization of processes. The technical feasibility was demonstrated by an implementation in a modular system with production-related logistics processes.

In the further work, additional implementations for evaluation will be developed. This will includes extended use and combination of different data sources as well as new methods for visualization of data and providing relevant information for CPS in Industry 4.0.

Fig. 1 .

 1 Fig. 1. Complete process of the approach

  support Connection of camera-based sensor data with production and logistics data


Alexandros Bousdekis

Gregoris Mentzas
email: gmentzas@mail.ntua.gr

Condition-based Predictive Maintenance in the Frame of Industry 4.0

Keywords: Proactive Maintenance, E-maintenance, Event processing

The emergence of Industry 4.0 leads to the optimization of all the industrial operations management. Maintenance is a key operation function, since it contributes significantly to the business performance. However, the definition and conceptualization of Condition-based Predictive Maintenance (CPM) in the frame of Industry 4.0 is not clear yet. In the current paper, we: (i) explicitly define CPM in the frame of Industry 4.0 (alternatively referred as Proactive Maintenance); (ii) develop a unified approach for its implementation; and, (iii) provide a conceptual architecture for associated information systems.

Introduction

Industry 4.0 indicates the flexibility that exists in value-creating networks which enables machines and plants to adapt their behavior to changing orders and operating conditions through self-optimization and reconfiguration. To do this, they perceive information, derive findings and change their behavior accordingly. At the same time, they store knowledge gained from experience with the aim to implement distributed and interconnected production facilities in future smart factories [START_REF] Lee | A cyber-physical systems architecture for industry 4.0based manufacturing systems[END_REF]. Industry 4.0 combines computer science with electrical and mechanical engineering and is considered to be a revolution on a business level and an evolution on a technological level.

Maintenance is a key operation function, since it is related to all the manufacturing processes and focuses not only on avoiding the equipment breakdown but also on improving business performance in terms of productivity, quality and logistics management [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF][START_REF] Fumagalli | Integrating maintenance within the production process through a flexible E-maintenance platform[END_REF]. Existing maintenance strategies can broadly be distinguished to three categories: (a) Breakdown Maintenance; (b) Time-based Preventive Maintenance; (c) Condition-based Predictive Maintenance (CPM) [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF]. Since products have become more and more complex due to the evolution of technology, the costs of time-based preventive maintenance have increased and CPM has evolved as a novel lever for maintenance management [START_REF] Guillén | A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies[END_REF]. However, there is not a clear classification in literature, while CPM in the context of Industry 4.0 has not been concretely defined. Currently, even large manufacturing companies have not developed a complete CPM strategy and appropriate sensor-driven, real-time systems in order to utilize its benefits [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF].

Existing CPM solutions suffer from several limitations: (i) Most of them focus on product maintenance, i.e. on the service stage of the Product Lifecycle Management (PLM) (e.g. warranty failures) and not on industrial maintenance, i.e. on the manufacturing stage of the PLM; (ii) They are mainly based upon physical, domain-specific models that are not easily extensible for other equipment or for other industries; (iii) They rarely exploit big data processing infrastructures for real-time, sensor data, since they usually use batches of data, while the level of data analytics maturity is usually low; (iv) Each one of them focuses on a specific aspect of CPM (e.g. condition monitoring, diagnostics, etc.) instead of having a unified approach for covering all the phases and industrial operations-related aspects.

The objectives of the current paper are: (i) to explicitly define CPM in the frame of Industry 4.0; (ii) to develop a unified approach for its implementation; and, (iii) to provide a generic reference conceptual architecture for the development of associated information systems. The rest of the paper is organized as follows. Section 2 discusses the literature review. Section 3 includes the definition of CPM in the frame of Industry 4.0, i.e. Proactive Maintenance. Section 4 outlines a unified approach for Proactive Maintenance implementation, while Section 5 presents a generic reference conceptual architecture. Section 6 concludes the paper and discusses the future work.

2

Literature Review

Condition-based Predictive Maintenance and E-maintenance

CPM is an evolving maintenance strategy that is increasingly gathering the interest of modern manufacturing companies by utilizing the capabilities of condition monitoring. Condition monitoring is usually realized with equipment-installed sensors, which have the capability of measuring with high frequency a multitude of parameters [START_REF]Condition monitoring and diagnosis of machines-Data interpretation and diagnosis techniques-Part 1: General guidelines[END_REF] leading to processing and storage of a huge amount of data. Big data pose challenges to the subsequent processing pipeline of data analysis, knowledge extraction and decision making [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF]. The classical industrial view of CPM is mainly focused on the use of condition monitoring techniques such as vibration analysis, thermography, acoustic emission or tribology [START_REF]Condition monitoring and diagnosis of machines-Data interpretation and diagnosis techniques-Part 1: General guidelines[END_REF]. The recent developments of maintenance management lead to a physical understanding of a system's useful life and health state prediction through dynamic pattern recognition in various available data sources. Despite the plethora of CPM frameworks existing both in the academic and the industrial realms, there is still a large gap for effective implementation of CPM extensively in industry, mainly due to complexity of these solutions and their life cycle [START_REF] Guillén | A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies[END_REF].

E-maintenance refers to the convergence of emerging information and communication technologies with systems which take into account the resources, services and management to enable decision making in a proactive way [START_REF] Fumagalli | Integrating maintenance within the production process through a flexible E-maintenance platform[END_REF] and thus, to enhance and extend the CPM framework [START_REF] Guillén | A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies[END_REF]. E-maintenance incorporates emerging technologies capable of optimizing maintenance-related workflows and integrating business performance with other components of e-enterprise [START_REF] Guillén | A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies[END_REF]. The development of emaintenance systems can be distinguished in two chronological periods which have different characteristics. The first wave of appearance, development and deployment of e-maintenance concepts and prototypes was during the period 2003-2008 (see e.g. [START_REF] Djurdjanovic | Watchdog Agent-an infotronics-based prognostics approach for product performance degradation assessment and prediction[END_REF][START_REF] Bangemann | PROTEUS-Creating distributed maintenance systems through an integration platform[END_REF][START_REF] Levrat | E-maintenance: review and conceptual framework[END_REF]). The second wave of e-maintenance paradigms has appeared in 2014 due to the emerging opportunities of the Industrial Internet of Things (IIoT), big data infrastructures and communication devices, but also due to the increasing financial pressures leading to a significant demand of eliminating maintenance costs (see e.g. [START_REF] Papa | Cyber physical system based proactive collaborative maintenance[END_REF][START_REF] Oelker | Preactive Maintenance-A Modernized Approach for Efficient Operation of Offshore Wind Turbines[END_REF]).

Industrial Internet of Things, Big Data and Proactive Event Processing

A CPM strategy implementation requires a complete methodology as well as appropriate information systems capable of processing information captured by sensors in order to provide added value insights [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF]. It can take advantage of IIoT advances for handling failure uncertainty, since the stochastic nature of the equipment degradation process leads to high uncertainty in decision making [START_REF] Van Horenbeek | Joint maintenance and inventory optimization systems: A review[END_REF]. Since the manufacturing domain is driven by events gathered through sensors, there is the need for event monitoring and big data processing information systems [START_REF] Babiceanu | Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook[END_REF]. Modern manufacturing companies have started to collect and store operations-related data or even utilize technological infrastructures and information systems for monitoring various parameters that are known to affect equipment condition and detecting early warning signals that machines or systems are degrading or in danger of breakdown [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF]. However, the strategic value of data analysis in the modern dynamic manufacturing enterprises should be increased by augmenting the level of data analytics maturity, through efficiently processing of real-time big data and supporting multi-directional knowledge flows, in order to enable the maintenance strategy transformation.

Proactivity in terms of information systems is driven by predictions, leading to increased situation awareness and decision making capabilities ahead of time. Proactive computing extends the reactive pattern known as 'Sense-and-Respond' [START_REF] Alexopoulos | A concept for context-aware computing in manufacturing: the white goods case[END_REF] or 'Detect-and-Act' [START_REF] Schulte | How to choose design patterns for event processing applications[END_REF] to a novel pattern which consists of four phases [START_REF] Engel | A basic model for proactive event-driven computing[END_REF]: Detect, Predict, Decide, Act. This proactive principle can be the base for a unified CPM framework and for its implementation with an appropriate information system by mapping the maintenance and other industrial operations (e.g. warehouse management, production planning, quality improvement) to these phases.

Definition of Proactive Maintenance

CPM in the frame of Industry 4.0 should be clearly defined taking into account the state-of-the-art theories, concepts and technologies in order to pave the way for the complete transformation of manufacturing enterprises from reactive to proactive. We argue that Proactive Maintenance indicates the CPM in the frame of Industry 4.0 in the sense that it brings together maintenance management and proactivity in terms of information systems. Proactive Maintenance has a managerial and a technological perspective. From a managerial point of view, its implementation requires the identification of the need for a different maintenance strategy through feasibility studies [START_REF] Bousdekis | A Multiple Criteria Approach Using ELECTRE for the Selection of Maintenance Strategy in Manufacturing Companies[END_REF] as well as the radical change of maintenance-related business processes and op-erations in all the enterprise organizational levels (operational, management, strategic). From a technological point of view, it requires appropriate technologies and information systems for effectively supporting the Industry 4.0 principles. Therefore, Proactive Maintenance should include the following characteristics:  IoT-based Condition Monitoring. Condition monitoring is applied with sensors at a component, machine or production process level. The decisions about their type and their distribution (placement) are affected by the manufacturing system examined. These hardware and/or software sensors generate huge amounts of realtime data (big data) in the context of IIoT which are further processed through appropriate infrastructures. The term "predictive maintenance" that is often used does not necessarily include real-time condition monitoring through sensors.  Event-driven Architecture. Event processing is used to process massive primitive events and get valuable high level information from them by continuously monitoring the event flow. Therefore, through the event triggers, event-driven infrastructures are able to handle big data in a scalable and efficient way.  Prognosis Lifecycle. Prognostic lifecycle covers all the maintenance phases, through which information is processed; from signal processing and diagnostics till prognostics and maintenance decision making along with continuous improvement during actions implementation. Predictions about the future equipment condition, on the basis of which mitigating actions can be applied ahead of time, constitute the backbone of CPM. They can be realized with associated predictive event processing agents. The term "Condition Based Maintenance" that is usually used does not necessarily include predictions, since it may refer to (near) real-time diagnostic outcomes, i.e. detection of the current condition.  Proactive Computing. Proactive event processing makes it possible to anticipate potential issues during process execution and thereby enables proactive process management, i.e. to decide and act on the basis of real-time predictions. The proactive event-driven applications are subjected to the proactive principle. A proactive situation includes a future event, a predictive pattern, the probability distribution function of the event occurrence, a list of mitigating actions and costs (e.g. the cost of the future event, the costs of actions as function of implementation time) [START_REF] Engel | A basic model for proactive event-driven computing[END_REF].  E-maintenance Support. The e-maintenance concept is linked to the CPM framework, since it provides the communication and technological background for real-time data processing and information exposure to the users and thus, it can support all the phases of the proactive principle. E-maintenance applications and platforms can facilitate proactivity and further advance to a greater value with the development of Cyber-Physical Systems, while they are able to utilize an eventdriven architecture for scalable sensor-generated big data processing.  Interaction with other Industrial Operations. Since, every change in industrial operations affects the others, CPM should be considered along with its interactions with the other operations. A reduction in production, quality and inventory costs is considered as one of the most important indirect benefits of CPM [START_REF] Van Horenbeek | Joint maintenance and inventory optimization systems: A review[END_REF]. For instance, due to the available real-time prognostic information, predictive maintenance actions along with quality improvement and production activities can be recommended and spare parts can be ordered just in time.

A Unified Approach for Proactive Maintenance

The proposed unified approach combines and extends existing CPM approaches [START_REF] Bousdekis | A proactive decision making framework for condition-based maintenance[END_REF]. It aims to frame and conceptualize Proactive Maintenance in order to enable manufacturing companies to fully exploit the availability of huge amounts of realtime and historical data with respect to the implementation of Proactive Maintenance by advancing their data analytics maturity. Unification can be achieved by bringing together approaches, tools and services each one of which implements a different phase of the Proactive Maintenance framework in order to effectively support different enterprise management layers, i.e. operational, management, strategic. Moreover, it enables data aggregation and interpretation as well as information sharing throughout the whole organization, both horizontally and vertically.

The systematic representation of any Proactive Maintenance solution allows its application in the production process of any manufacturing company regardless their processes, products and physical model used, while additional aspects (e.g. company policies) can be addressed with context-awareness. The proposed framework aims to cover the whole prognostic lifecycle along with their interactions with quality management, production planning and logistics decisions. The overall unified approach for Proactive Maintenance is depicted graphically in Fig. 1. Each conceptual block should incorporate an e-maintenance service or tool implementing appropriate methods and algorithms. The conceptual blocks are communicated among them through events. The event-driven approach enables the combination of model-based, knowledge-based and data-driven algorithms and methods existing in each conceptual block provided that each one is able to provide the required output that the next one requires. The scope of each conceptual block is explained below:  Signal Processing: It consists of the "Data Acquisition" and "Data Manipulation" steps, being performed in a modular way utilizing new emerging technologies. It should be able to connect and acquire data from heterogeneous data sources and integrate them for further processing.  Diagnosis: It consists of the "State Detection" and "Health Assessment" steps. It may include real-time model-based, knowledge-based and/or data-driven methods, embedded in appropriate and continuously processing software in order to recognize the presence of an unusual state with respect to a model of 'normal' behavior.  Prognosis: It includes state prediction of a whole system or components with respect to a mechanical system, e.g. prediction about the time-to-failure and the probability distribution function of the failure occurrence, the Remaining Useful Life (RUL), the Remaining Life Distribution (RLD). The analysis can be carried out by model-based, knowledge-based and/or data-driven algorithms.  Maintenance Decision Making: It includes decision making algorithms for taking decisions automatically or for providing recommendations ahead of time, on the basis of real-time prognostic information and maintenance-related expert knowledge. Thus, the optimal mitigating (perfect or imperfect) maintenance actions and the optimal times for their implementation are derived. The decisions can be continuously improved through a Sensor-Enabled Feedback (SEF) mechanism.

 Maintenance Action Implementation: It includes a real-time feedback mechanism for gathering, storing, aggregating, analyzing and visualizing data and information with respect to the actual actions implementation. The actions are implemented based on the generated decisions either automatically or by humans receiving the recommendations. The sensor-generated data are further processed in order to continuously update and improve the diagnostic, prognostic (through FMECA) and decision (through SEF) models and their continuous learning capability.  FMECA: Apart from their visualization, in order to further be processed, the actions-related data and information feed into the Failures Modes Effects and Criticality Analysis (FMECA) mechanism, which incorporates algorithms for the identification of potentially relevant and critical failures modes. FMECA can be implemented either on a physical model base or on a data analytics base.  Industrial Operations Management: It is based upon data and information about production planning, logistics management and quality management. They are stored in company's systems (e.g. ERP, MES) and are gathered in order to consider the industrial operations affected by maintenance processes (e.g. quality levels, production activities, spare parts inventory) and optimize the overall business performance. The data are processed according to the proactive principle and interact with the e-maintenance services. Examples of their utilization could be productionrelated predictions, joint maintenance and inventory optimization algorithms, quality-related constraints in the maintenance utility function optimization, etc. The conceptual architecture of a Proactive Maintenance intelligent information system is shown in Fig. 2. It consists of 3 layers which are further described below:  User Interaction Layer. The user interaction layer occupies the top level of the architecture and includes a configuration and visualization dashboard that supports the configuration of the architecture and the embodiment of the appropriate expert knowledge, as well as the visualization of the current and the predicted equipment behavior, the generated recommendations, etc. with the use of appropriate graphs.  Real-time Processing Layer. The real-time processing layer deals with the continuous processing of sensor data according to the Proactive Maintenance framework by applying the proactive principle of information systems in both the emaintenance and e-operations services, which can interact between them with the aim to schedule the maintenance activities together with the production, the quality and the logistics ones.  Data Layer. The data layer of the architecture includes a Database Abstraction Layer and houses a relational database engine where all information needed by the other two layers is stored and retrieved. In the current paper, we explicitly defined CPM in the frame of Industry 4.0, which can be referred as Proactive Maintenance, we developed a unified approach for its implementation and we provided a generic reference conceptual architecture for the development of Proactive Maintenance systems with the aim to maximize the expected utility of manufacturing firms and to exploit the full potential of predictive maintenance management, condition monitoring, sensor-generated big data processing, e-maintenance and proactive computing. Regarding our future work, we plan to develop a unified Proactive Maintenance information system by using, extending, developing and integrating new e-maintenance services and tools addressing the various phases of the aforementioned framework for exploiting the full potential of Proactive Maintenance. Then, we aim to validate our approach in industrial environment.

Fig. 1 .

 1 Fig. 1. The Unified Approach for Proactive Maintenance

Fig. 2 .

 2 Fig. 2. The Conceptual Architecture of a Proactive Maintenance System

Acknowledgements. This work is partly funded by the European Commission project FP7 ProaSense "The Proactive Sensing Enterprise" (612329).


Günther Schuh
email: guenther.schuh@fir.rwth-aachen.de

Marcel Schwartz
email: marcel.schwartz@fir.rwth-aachen.de

Prerequisites for the Successful Launch of Enterprise Social Networks

Keywords: Social network, social software, enterprise social network, sociotechnical system, human-technology-organisation concept

The importance of social networks and, in particular, enterprise social networks in business contexts is increasing significantly. Regarding the prerequisites for a successful implementation of an enterprise social network, exclusively providing the technical infrastructure is insufficient. A holistic view that considers and integrates different perspectives is crucial for success. This includes technological, organisational and human aspects as equally important parts of the network. This paper identifies prerequisites for a successful launch of enterprise social networks and groups them along these three dimensions.

Introduction

Technological developments have changed the way humans interact with each other. Replacing bilateral communication, e.g. on the phone, information is increasingly shared via social networks. Such networks have become a permanent feature in most of our private daily lives. Facebook counts 1.6 billion accounts, and 1 billion people use the instant messaging service WhatsApp. YouTube, Instagram, Twitter and Skype are also used by millions of people [START_REF] Manyika | Digital Globalization: The New Era of Global Flows[END_REF]. In 2015, a total of 2.14 billion people were active in social networks [2]. With some delay, social networks have also become more important in a business context: Companies like Microsoft, IBM or Deloitte have established enterprise social networks (ESN) to increase their ability to innovate as well as their employees' productivity [START_REF] Brandel | The new employee connection: Social networking behind the firewall[END_REF].

Regarding future developments, the significance of enterprise social networks will keep increasing as more business leaders recognize their potentials to improve company internal communication, collaboration and innovation processes [START_REF] Bughin | How social tools can reshape the organization[END_REF]. Those potentials include the following: ESNs allow to connect employees with each other according to specific needs. They help to reduce hierarchical barriers between employees and thereby improve knowledge transfer within the company [START_REF] Bloching | Wer teilt gewinnt: Zehn Thesen, wie Digitalisierung und Social Media unsere Unternehmen verändern[END_REF]. All employees can be involved in the innovation process regardless of barriers of time or location. For these reasons, new solutions can be developed faster and with a higher chance of success [START_REF] Bloching | Wer teilt gewinnt: Zehn Thesen, wie Digitalisierung und Social Media unsere Unternehmen verändern[END_REF]. Another aspect is that ESNs enable companies to uncover possibilities and risks earlier than is possible within traditional hierarchical structures [START_REF] Bloching | Wer teilt gewinnt: Zehn Thesen, wie Digitalisierung und Social Media unsere Unternehmen verändern[END_REF]. In addition, person-specific knowledge can be shared more easily [START_REF] Schmitz-Urban | Entwicklung einer Morphologie für Business-Communities[END_REF]. In conclusion, ESNs have the potential to enhance the capability of companies to innovate and to increase work efficiency.

In order to exploit all existing potentials, the challenges associated with the launch of an ESN have to be addressed and overcome. This includes aspects such as data security [7], internal conflicts (e.g. conflicts with the works council or the company's management) [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF], or unsuitable or lack of staff responsible for the operative management of the network [START_REF] Iriberri | A Life-Cycle Perspective on Online Community Success[END_REF]. The assumption that the only necessary step is the implementation of a suitable information and communications technology infrastructure and that employees will use the network without any further steps is considered untenable nowadays [START_REF] Happe | The State of Community Management 2015: Harvesting the Rewards of Community[END_REF]. Many authors emphasize the importance of a structured network management which must be planned long before the IT infrastructure is implemented [START_REF] Iriberri | A Life-Cycle Perspective on Online Community Success[END_REF]. This leads to the central research question to be answered in this paper: What are the relevant prerequisites a company has to meet to successfully launch an Enterprise Social Network?

To answer this question, a short overview of the relevant definitions and the state of the art in related research is given, followed by a description of the applied methodology. Thereafter the research results are pointed out and thematically clustered. The paper concludes with a summary including some examples of how the relevant prerequisites can actually be met and an outlook on possible future research.

Definitions and State of the Art

In a business context, ESNs can be distinguished by both their purpose and target group. Regarding their purpose, ESNs can be differentiated by whether they focus on cooperation/collaboration or on innovation processes. In terms of target groups, ESN can be aligned with either company-internal or external users. Although the ESN typically has one of the above-named focuses, these are not necessarily exclusive, e.g. networks that focus on collaborative processes can also, as a side effect, foster innovation [START_REF] Schuh | Erfolgreiche Steuerung und Koordination von Business-Communitys: Whitepaper[END_REF]. The application of ESNs mainly targeted at company-external stakeholders, e.g. to support customer relations, is increasingly important. This paper, however, focuses on ESNs essentially designed for company-internal users whose main purpose is to support processes of cooperation and collaboration [START_REF] Chin | Exploring Factors Influencing the Use of Enterprise Social Networks in Multinational Professional Service Firms[END_REF].

The above categorization results from a socio-scientific and organisational perspective on the topic. Focussing on the technological aspect, the term 'social software' is used for web-based solutions that enable communication, cooperation, coordination and collaboration in the context of networking of people [START_REF]Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software[END_REF].

Both perspectives have to be considered as equally important. Consequently, ESNs can be interpreted as socio-technical systems which can be defined as systems that neither exclusively consist of technical (plant, machinery) nor of social (work and process organisation) sub-systems but rather of a combination of both system types [START_REF] Kelly | A Reappraisal of Sociotechnical Systems Theory[END_REF].

The current state of the art in research on ESNs is shaped by different approaches that have a particular focus on either socio-scientific, organisational or technological perspectives concerning their implementation and utilization in companies. This paper, in addition, provides a holistic view of the topic that integrates and expands the results of existing research. Moreover, this paper can also be used as a guideline for companies seeking to implement an ESN.

Methodology

For the introduction of an ESN, a number of different challenges have to be met. Therefore a suitable reference framework has to be selected in which the factors for a successful launch can be structured and classified.

Following an extensive literature review, a holistic analytical approach to networks was chosen, based on the view of ESNs as socio-technical systems and, additionally, including the human element as a crucial factor. This approach, known as Human-Technology-Organisation Analysis, considers technology, organisational aspects and human individuals to be equally important parts of a well-functioning system. It places emphasis on the interactions between the individual elements and foregrounds the distribution of tasks between humans and technology (human-machine interface) [START_REF] Ulich | Arbeitspsychologie[END_REF].

Applying Human-Technology-Organisation Analysis to the research of prerequisites required for a successful launch of an ESN, an in-depth literature research was carried out as a first step to gain a profound theoretical understanding. The results were discussed with experts from several companies of different sizes and business sectors that are very experienced in the application of ESNs. In discussions and workshops including structured interviews, using predefined questionnaires, it was tested whether the results represent actual company practices. As a next step, the results were further elaborated. The identified prerequisites were categorized along three dimensions and subsequently grouped into topic-specific clusters. This provides a systematic overview of the central aspects to be considered, which should be helpful to users later on. In order to ensure the correctness of the results, they were finally validated by experts. The entire process can be illustrated as follows [16]:

Fig

Results

Following a Human-Technology-Organisation Analysis approach, the identified prerequisites for a successful launch of the ESN can be categorized along the dimensions of human, technology and organisation. The results, which are described in detail below, can be illustrated as follows:

Human Dimension

Within the human dimension, the prerequisites companies have to meet can be grouped into four thematic clusters.

First, employees should not be afraid of using the new ESN [7]. Reservations and anxieties about using the network may be due to various reasons: Possibly, they result from insecurity about new working methods which require sharing of non-finalised work results [START_REF] Bukvova | T-Systems Multimedia Solutions: Vernetztes Arbeiten im Team Web[END_REF]. In addition, it is unusual for employees to comment publicly on their colleagues' or supervisor's work or to criticize it openly [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. To overcome this, a positive and open error management must be established [START_REF]Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software[END_REF]. Employees may also be worried of losing control of their knowledge or of surveillance [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. To prevent any complications, the company needs to alleviate worries that employees might have from the very beginning by establishing transparent work routines and structured communication [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. Another prerequisite resulting from this is explained by Murschall [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]: "Members of staff should be prepared to let their sovereignty of information go and share their knowledge with others. If the employees participate actively in the network, its overall benefit increases and the network is more likely to be accepted by its users."

Another crucial requirement for success is that all affected employees have the basic knowledge needed to operate the new medium [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF] Fig. 2: Prerequisites for a successful implementation of enterprise social networks, clustered following a Human-Technology-Organisation Analysis approach is important to make decisions regarding the length, frequency and visibility of specific information that is shared via the ESN. At the same time, users also need to be aware of the benefits as well as the limits of the network [18]. It is important to know when a traditional telephone call or face-to-face meeting might be a better choice.

A further requirement for successful implementation is that there is fundamental clarity about and awareness of rules for members of staff [START_REF] Ahlheid | Konzepte für ein communitybasiertes Wissensmanagement im Enterprise 2[END_REF]. Every employee needs to know how much time he/she should spend on using the ESN and what kind of information is to be shared to what extent [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. In order to fulfil these prerequisites, companies should clearly define what they expect from the use of the network and communicate to the team the goals they want to achieve. This can be realized through participation, content, or code of conduct guidelines [20]. In addition, the benefits for employees must be clear to them, otherwise the ESN will not be used [START_REF] Wenger | Cultivating communities of practice: A guide to managing knowledge[END_REF].

The network's success also requires that responsibilities for network management and moderation are defined early on [START_REF] Iriberri | A Life-Cycle Perspective on Online Community Success[END_REF]. There is now a consensus that the establishment and development of an ESN is a process that must be moderated and guided.

Technical Dimension

The technical prerequisites can be grouped in three thematic clusters. First, for the successful launch of an ESN, an appropriate infrastructure must be in place [START_REF] Schmitz-Urban | Entwicklung einer Morphologie für Business-Communities[END_REF]. This includes the availability of a high data transmission rate and data storage capacity [START_REF] Wenger | Cultivating communities of practice: A guide to managing knowledge[END_REF]. The ESN must be technically stable and react fast, if needed [START_REF] Iriberri | A Life-Cycle Perspective on Online Community Success[END_REF].

In terms of hardware, personal computers and/or mobile devices [START_REF] Happe | launch a customer community based on business ROI and customer needs, integrate it into your channels, pocesses and systems, and[END_REF] will be required as well as servers [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. Because of the increasing use of mobile devices such as smartphones or tablets, data can be saved on cloud-based servers [START_REF] Happe | launch a customer community based on business ROI and customer needs, integrate it into your channels, pocesses and systems, and[END_REF]. Sensitive data or company secrets should, however, be saved on an extra server and have a unique decoding system for additional protection [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. The firewall settings have to be adjusted so that mobile access is possible from outside the company's network.

Furthermore, the choice of software and software functions is an equally important prerequisite for the success of an ESN [20]. Companies may choose to use standard software, which is generally cheaper to acquire and offers basic but stable functions [START_REF] Schmitz-Urban | Entwicklung einer Morphologie für Business-Communities[END_REF]. They may, however, also decide to develop their own software. This comes with a higher workload and effort but enables the company to implement specific functions according to their needs and to use company-specific vocabulary, if desired [START_REF] Schmitz-Urban | Entwicklung einer Morphologie für Business-Communities[END_REF]. Regarding the design of the user interface of the network, it needs to make sure that important topics are highlighted and that a useful content structure exists [START_REF] Wenger | Cultivating communities of practice: A guide to managing knowledge[END_REF]. All in all, the software should be easy and intuitive to use [START_REF]Web 2.0 in der Unternehmenspraxis: Grundlagen, Fallstudien und Trends zum Einsatz von Social Software[END_REF]. Moreover, it must be possible to extend the software and add functions, if necessary. This is crucial as the goals of the network may change over time [START_REF] Wenger | Cultivating communities of practice: A guide to managing knowledge[END_REF]. From a technological point of view, it is also important to integrate the ESN into already existing websites and systems [23].

Organisational Dimension

Regarding the organisational foundations that need to be laid, five thematic clusters can be identified.

It is of utmost importance that the company sets specific goals for the use of the network [20]. This should hold a strategic value and benefit for the company [24]. It is important to communicate the business objectives with the help of participation, content, or code of conduct guidelines, for example [20].

An additional prerequisite is to make sure that human and financial resources are available when needed [20]. Among other expenses, costs will arise for managers and moderators of the network, IT managers, and for the purchase of hard-and software [START_REF] Happe | The State of Community Management 2015: Harvesting the Rewards of Community[END_REF]. Furthermore, expenses for business trips, additional trainings or events that cover network-related topics may be necessary [24].

It is also mandatory to consider legal issues beforehand. One of the most important aspects in this context is the question of copyright. Additionally, industrial law and data security rights need to be considered [7]. The works council needs to confirm the appropriateness and agree to the use of the ESN [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF]. Apart from this it is crucial that no company secrets are passed on to third parties [START_REF] Hinchcliffe | The state of Enterprise 2.0[END_REF].

In addition, a relevant factor for the success of an ESN is an open company culture [START_REF] Ahlheid | Konzepte für ein communitybasiertes Wissensmanagement im Enterprise 2[END_REF]. To establish such a culture, organisational barriers need to be reduced. This means that hierarchical structures need to be transformed into connected network structures [18]. Members of staff need to be ready to trust each other and exchange ideas instead of keeping them to themselves to strengthen their own position [START_REF] Müller | Siemens Building Technologies Division: Globaler Wissens-und Erfahrungsaustausch mit References+[END_REF]. An open work culture also means that employees are allowed to communicate constructive criticism across all hierarchy levels [START_REF] Murschall | Soziales Intranet und Social Collaboration: Ein Erfahrungsbericht[END_REF].

Eventually it is important for the participation of employees that the company's management exemplifies the use of the network [START_REF] Müller | Siemens Building Technologies Division: Globaler Wissens-und Erfahrungsaustausch mit References+[END_REF]. Therefore, the visible support from executives is another factor for success [20].

Conclusion

In this paper, prerequisites for the successful launch of an ESN were identified. In order to illustrate them and to facilitate their use in practice, Human-Technology-Organisation Analysis was used as a frame of reference. The essential advantage of this approach is that related social, technical and organisational challenges are integrated and considered simultaneously. For each of the three dimensions, a number of prerequisites were identified and subsequently grouped into thematic clusters.

Within the human dimension, it is important to allay employees' fears of using the ESN and to enable them to use it by preparing them sufficiently, e.g. in personal talks with network managers, who should also serve as contacts if users need further support. In addition, the rules and requirements of the network need to be made transparent, e.g. by providing trainings or short marketing videos. Furthermore, persons responsible for the management and networking of members of staff are to be appointed. On top of that, one of the most crucial factors is to clarify the benefits of using the ESN for the users' everyday work.

Focusing on technological aspects, companies need to provide a suitable network infrastructure and hardware and select an appropriate software solution. In this context it is most relevant to ensure the usability of the software, especially if the age structure of the employees is heterogeneous.

Regarding the organisational dimension, the following aspects should be considered: The objectives that are to be achieved by using the network need to be defined and communicated to all those involved. It is necessary that personnel and financial resources are available and potential legal issues are clarified in advance. As a final and most relevant prerequisite for the successful launch of an ESN, the management has to support an open business culture and promote the use of the network, including actively using the network itself.

This paper can serve as a guide for enterprises willing to foster intra-organisational collaboration via ESNs, as it provides a structured overview of relevant prerequisites for a successful implementation. As the paper does not focus on the description and analysis of best-practice approaches of companies that have already successfully launched an ESN, this could be a topic for future research.

. 1 :

 1 Explorative Research Process[16]

		Questions to
	Personal		Reality		Research and
	Experience				Consulting Projects
	Theoretical back-ground Knowledge	Theoretical Understanding	Details of	Data Collection	Interviews with Experts
			the Heuristic
			Frame of
	Literature Analysis		Reference		Case Studies
		Differentiation	Critical Reflection
		and Abstraction		of Reality

  . They need to know which functions offer what kind of benefits and what kind of content is useful to share. Additionally, it

	Human
	▪ no/minor fear of contact
	▪ basic knowledge of the use of social software
	▪ awareness of rules and expectations
	▪ clear responsibilities for moderation and
	network management
	▪ infrastructure for the information and communications technology ▪ hardware (PCs and mobile devices) ▪ choice of software and its functions	▪ definition and communication of goals ▪ availability of human and financial resources ▪ solutions for legal issues ▪ support of an open company culture ▪ noticeable support of the network from the
	Technology	management	Organisation

Acknowledgements

The research results presented in this paper were partly developed as part of the ToMiC research project. This project is sponsored by the AiF under the program for the promotion of industrial research and development (IGF) by the German Federal Ministry for Economic Affairs and Energy under the number 18508 N.


Dimitris Mourtzis
email: mourtzis@lms.mech.upatras.gr

Ekaterini Vlachou

Vasilios Zogopoulos

Xanthi Fotini

Integrated Production and Maintenance Scheduling Through Machine Monitoring and Augmented Reality

Keywords: An Industry 4.0 Approach Maintenance, Scheduling, Augmented Reality, Machine monitoring, Industry 4.0

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

As modern manufacturing marches towards Industry 4.0, unified solutions that integrate Information and Communication technology (ICT) and sensors while remaining agile and adaptive to environment conditions are being developed, updating physical systems to cyber physical systems [START_REF] Kadiri | Current trends on ICT technologies for enterprise information systems[END_REF]. Currently, production scheduling software packages are used by the majority of manufacturing companies. Though capable of supporting the production in an adequate level, they fail to integrate production line disturbances, thus creating inaccuracies in high-altering systems [START_REF] Mourtzis | The evolution of manufacturing systems: From craftsmanship to the era of customisation[END_REF]. One of the main causes of changes in the schedule is maintenance tasks, which, though they are a common part of the machine's lifecycle, are not reported back for rescheduling. Event-driven rescheduling could dictate a more flexible and close to the real system method of tackling this issue [START_REF] Mehta | Predictable scheduling of a single machine subject to breakdowns[END_REF][START_REF] Chryssolouris | Manufacturing Systems: Theory and Practice[END_REF].

Maintenance is an important part of products' lifecycle, accounting for a high percentage of its total cost [START_REF] Dhilon | Maintenability, Maintenance, and Reliability for Engineers[END_REF] and having a high impact on the utilization period of the machine. In the last few years, manufacturing companies have started adopting monitoring solutions in order to increase system reliability through reducing the unplanned breakdowns. Machine condition monitoring systems are being implemented in modern machinery informing human operators for the majority of the upcoming maintenance tasks, based on Wireless Sensor Network (WSN) input [START_REF] Giannoulis | Wireless sensor network technologies for condition monitoring of industrial assets[END_REF], defining the remaining operating time between failure of the machine tools [START_REF] Mourtzis | A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring[END_REF]. Moreover, the last few years, new technologies are starting to be implemented in maintenance processes in order to increase its efficiency and facilitate the maintenance support service provision. Augmented Reality is one of the upcoming technologies in manufacturing [START_REF] Nee | Augmented Reality applications in design and manufacturing[END_REF] with numerous applications aiming to provide remote maintenance support [START_REF] Mori | Development of Remote Monitoring and Maintenance System for Machine Tools[END_REF][START_REF] Platonov | A mobile markerless AR system for maintenance and repair[END_REF], in an effort to facilitate technical knowledge distribution in a digitalized and easy-to-perceive way. Additionally, it poses a digitalized way of bridging the gap between the CAD files the maintenance instructions manuals.To facilitate system communication, cloud platforms have emerged as a viable solution in manufacturing systems, as they enable the seamless data and knowledge exchange from different systems embedded in the shop-floor and its users [START_REF] Xu | From cloud computing to cloud manufacturing[END_REF]. Moreover, high security of these systems makes them a viable solution to combine with different wireless sensor networks, providing unified solutions of monitoring and acting on the production line by bringing together different systems and stakeholders [START_REF] Mourtzis | A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring[END_REF].

The literature review makes apparent that although there is a lot of progress in implementing isolated solutions for machine monitoring, scheduling and maintenance support, there are limited unified approaches integrating communication between operational planning and maintenance planning [START_REF] Papakostas | An approach to operational aircraft maintenance planning[END_REF]. Aiming to create an integrated framework that, following the concept of Industry 4.0, will bring together these systems, an application that will support production and maintenance scheduling through machine monitoring and Augmented Reality maintenance instructions is developed. The developed application integrates all the necessary connections that will allow the machine operator to monitor the status of the machine, schedule maintenance tasks and communicate with a maintenance expert so as to receive AR maintenance instructions remotely. The developed application is applied in a CNC milling machine use case.

Integrated Production and Maintenance scheduling via machine monitoring and AR technology

This work proposes an integrated solution for combined production and maintenance scheduling, by receiving input from a machine monitoring system. The framework combines three major functionalities: (i) machine health status visualization, including the remaining operating time between failures (ROTBF), (ii) maintenance scheduling based on current production schedule and (iii) remote maintenance support supported by Augmented Reality. All the functionalities are bundled in a mobile device application that is used by the machine operator. The workflow that shows all the actions and the subsystems of the framework is presented below in Fig. 1.

Fig. 1. Workflow of the proposed framework

The first step of using the developed application is the status monitoring of the machine. The machine is equipped with a data acquisition (DAQ) device that monitors the status of the manufacturing resources and especially machine tools [START_REF] Mourtzis | Cloud-Based Adaptive Shop-Floor Scheduling Considering Machine Tool Availability[END_REF]. The monitoring device, through current and voltage sensors, gathers real time sensor data and transmits them through a wireless sensor network to a central database where the status of the machine (down, busy or idle) is detected and the actual machining time (AMT), the machine tool utilization and availability are calculated. Since the mean time between failures (MTBF) per machine is known as a technical characteristic, using the following Equation, the remaining operating time between failures (ROTBF) is calculated:

ROTBF = MTBF -AMT (1)
Open To connect the visualized results with each machine, an AR marker is placed on each one. The operator, using the developed application, scans the AR marker and calls the status information of the machine and the ROTBF in the GUI of the application. Then, based on the ROTBF of the machine, the operator may plan ahead the maintenance task. Whenever the ROTBF is below a preselected threshold k (e.g. 10%), the operator is notified that a maintenance task will be needed shortly. At this point, the operator calls, through the application, the machine schedule and the mean time to repair for the required process. The mean time to repair is either extracted by past maintenance tasks, if the same task has re-occurred or, by implementing sophisticated algorithms that may provide a trustworthy estimation, such as Case-based Reasoning (CBR) [START_REF] Mourtzis | Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks[END_REF]. Based on the estimated duration of the maintenance task and the machine schedule, the operator may either, if possible, plan the task for a timeslot when the machine is idle or plan it whenever the operator thinks is necessary despite of the current schedule; this feature is highly important, especially in cases of unexpected breakdowns. In both cases, the information related to the maintenance planning is returned back to the scheduling system. In the present approach, the remaining tasks of the machine are shifted right on the schedule, always checking if a task has to be shifted to another working shift or even another day. Moreover, the developed application supports the operator in dealing with maintenance procedures that the operator may have limited experience on. The operator, through the same application, receives the maintenance instructions by a maintenance expert that can be remote located, in a digitalized form, exploiting Augmented Reality [START_REF] Mourtzis | Cloud-Based Augmented Reality Remote Maintenance through Shop-Floor Monitoring: A Product-Service System Approach[END_REF]. The technician may connect to a cloud database that includes all the past AR maintenance instructions, organized per machine and named in an easy-to-perceive way, and download the required procedure. In case the malfunction is not listed in the database or the operator is not aware of the cause, a malfunction report is constituted by the operator and sent to the maintenance expert. The application allows the operator to write explanatory text, take photos and sound recordings that will facilitate the expert to diagnose the malfunction cause. This expert may either be from the machine manufacturer, an external specialized technician or even from the central maintenance department of the company. The expert diagnoses the malfunction cause and creates the AR maintenance instructions, which are sent back to the on-spot technician through the cloud database. To support this process, a series of pre-created part animations, GUIs and scripts have been developed. Moreover, an algorithm that breaks down the assembly CAD to assembly/ disassembly steps has been implemented; this way the instructions for the assembly processes of the maintenance task can be easily created, saving a lot of time since they account for a large part of the maintenance tasks [START_REF] Mourtzis | Cloud-Based Augmented Reality Remote Maintenance through Shop-Floor Monitoring: A Product-Service System Approach[END_REF]. The operator receives the AR instructions in the mobile device, performs the maintenance and validates that the machine is operational again.

System Implementation

In order to implement the machine monitoring, a data acquisition device was developed. It utilizes split-core current transformers as current sensors, a closed loop hall current sensor, voltage sensors, as well as a camera. A WSN is established so as to send the data from the DAQ for processing and to make them accessible by the integrated application. The WSN is facilitated with the use of DIGI XBee ZigBee RF module [START_REF]Zigbee[END_REF]. ZigBee is a specification of the IEEE 802.15.4 standard. The selection of ZigBee over other wireless standards is due to its support to various network topologies and encryption algorithms, and its robust operation with functionalities, such as collision avoidance, retries, and acknowledgments performed in the hardware. In order to derive the status of the machine, as well as the AMT, an information fusion technique consists of the Analytical Hierarchy Process (AHP) and the Dempster-Shafer (DS) theory of evidence [START_REF] Mourtzis | Cloud-Based Adaptive Shop-Floor Scheduling Considering Machine Tool Availability[END_REF] was applied. To develop the integrated application and also to create the AR maintenance instructions, two commercial software tools were used: Unity [18] and Vuforia [17]. The first one supports the creation of high quality and usability GUIs that guide the operator through the available functionalities and supports seamless connection with the cloud databases, while also allowing the application to be directly built as a mobile device app for Android OS. The latter, used as an add-on on the first, is used to add Augmented Reality features into the maintenance instructions and the monitoring application. Moreover, in an effort to support and partially automate the AR maintenance instructions generation process, an algorithm that breaks down the assembly CAD into steps of assembly and disassembly is implemented [START_REF] Mourtzis | Cloud-Based Augmented Reality Remote Maintenance through Shop-Floor Monitoring: A Product-Service System Approach[END_REF].

Fig. 2. Interfaces between the developed application and other tools and databases

Furthermore, the algorithm detects the axis and direction of assembly/ disassembly for each part thus allowing quick implementation of part animations in the AR scenes. An important part of the proposed framework is the integration and communication with various other platforms. The developed application connects to applications and cloud platforms via internet so as to send and receive data, creating a set of interconnected tools that function together; the data exchange between those tools can be seen in Fig. 2. The application receives the AMT as an input from the monitoring system through an xml file. When a maintenance task needs to be planned, the MTTR is either extracted by past maintenance tasks, if the same task has re-occurred or is estimated using similarity mechanisms and algorithms. Then, the machine schedule is called, updated with the task and returned back to the scheduling; both data exchanges are performed via xml. When sending a maintenance report, the operator sends text, photo and sound files and receives a sequence of AR scenes that can be directly loaded by the application.

Case study and Results

The proposed applications are validated in a case study including a three axis CNC machine tool XYZ SMX SLV. In the current procedure, the operator of the machine tool

Scheduling AMT ROTBF Malfunction Report

AR Maintenance Instructions Machine Schedule

Machine Schedule

Shop floor

Sensor Board Gateway

NoSQL

Monitoring System

Application Server

AR Instructions

Instructions Database

Occurred Maintenance tasks

Cloud

Mobile app for integrated production and maintenance scheduling

Web Server Java Page Server plans a maintenance task based on a breakdown that may occur, without considering any data from the machine tool. The maintenance department is informed on the maintenance issue and deploys the maintenance expert to go on spot to diagnose the issue and physically perform the maintenance task in the production plant. In an effort to eliminate this need and reduce maintenance time and cost, the proposed system is applied. The developed monitoring system considering the data acquisition device and the WSN was set up. The monitoring system gathered all the necessary data to identify the machine tools status, the actual machining time, as well as the ROTBF. The technician checks the AR instructions available in the cloud database.

Call machine schedule & MTTR

Instructions unavailable or unknown malfunction cause

Maintenance Instructions available

Create Malfunction report, including text, photos and sound recordings and sent to maintenance expert

Identify the possible timeslots and schedule maintenance Perform AR supported Maintenance Expert creates and uploads AR instructions Immediate Maintenance

Plan Later structions on how to change the cutting tool and the tool holder performing the maintenance task. Moreover, using the same application, the operator could plan the task for a later timeslot, and update the machine schedule. The sequence of actions followed by the technician are visualized in Fig. 3. By implementing this application on the shopfloor, the operator is capable of monitoring the status of the machine, pre-identifying malfunction and scheduling their repair, while updating the machine schedule in a way that is more efficient and easier to use than the currently existing methods, bundled into one software. In case of an unexpected maintenance task the operator through the proposed system calls the AR maintenance instructions and performs the maintenance tasks easily, quickly and with low cost. Based on the first implementation of the proposed tools, it is estimated that the awareness of machine tools' condition will be increased over 30% and the response time to changes will be decreased over 25%. Moving also towards Industry 4.0, the proposed approach enables the integration of different systems supporting integrated planning and control, increasing systems interoperability and efficiency.

Conclusion and Future work

The proposed work presents an integrated way of production and maintenance scheduling supported by augmented reality technology and real-time machine monitoring, aiming to move towards industry 4.0. The developed system exploits sensor data from production machines to perform condition-based maintenance and integrated scheduling and AR supported maintenance, implemented as a mobile device application. The system includes all the required connections that allow the application to receive data from the sensors so as to monitor the remaining operating time between failure, plan a maintenance task based on the available timeslots, update the machine schedule based on the maintenance task's duration and the connect to a cloud database of AR maintenance instructions. Moreover, in order to improve the AR maintenance instructions generation and secure the high quality of the generated result, the maintenance expert is supported by a set of functionalities that facilitate and accelerate the instructions generation process, based on an algorithm that breaks down the assembly tasks and precreated GUIs and animations. The proposed system increases system's interoperability, efficiency, and communication, providing useful data from the monitoring system which can be further analyzed and transform isolated planning and control systems into adaptive. As a step towards Industry 4.0, the proposed system integrates data from different sources (scheduling system, monitoring system) and supports the human operator through a mobile device to plan and perform maintenance tasks easily and efficiently, increasing system's productivity. Future work will be focused on enhancing the existing approach and predicting the ROTBF based on the sensory data. Moreover, it is important to connect the developed application to other mobile devices, such as smartwatches, in the production line keeping in mind the projected marching towards Industry 4.0. Finally, it is important to study the implementation of the system to other, more complex use cases.

Fig. 3 .

 3 Fig. 3. Application of the developed frameworkUsing the developed application, the technician could foresee the required maintenance task and plan it ahead in an available timeslot in the machine schedule. In this use case, unexcepted maintenance task is occurred, where the cutting tool and the tool holder needed replacement due to tool breakage. The operator directly connected to the AR maintenance instructions database, downloaded the corresponding maintenance in-

Close Application Maintenance Maintenance now or later? Get schedule Check for available windows Check MTTR Is there available window? Add Maintenance Task Plan Maintenance Shift forward Select Available Window Plan maintenance task Start Monitoring the machine tool Real time Sensor data capturing

	Monitoring System
	(Facility database and
	monitoring)	AR application
	Machine tools and
	Robots, cutting tools	Data Visualization/
	specifications	Machine Check
	Machine tool Operator input	Is maintenance
	Information Fusion	required?
	(AHP+DS)
	Calculate Status and Actual Machining time (AMT)	ROTBF >k?
	Calculate Remaining
	Operating Time Between
	Failures (ROTBF)
		Malfunction
		Report
		Is there
		similar
		existing
		report?
		Download AR
		scenes
		Download AR
		scenes

Visualize AR scenes Maintenance Send malfunction Report to expert Diagnosis Expert Creates AR Scenes Expert Uploads AR Scenes on cloud Scheduling NO YES YES YES Send info to scheduling NO NOW LATER NO Send Schedule YES MTTR Estimation NO Legend: AHP: Analytical Hierarchy process DS: Dempster-Shafer MTTR:Mean time to Repair AR: Augmented reality AMT: Actual machining time ROTBF: Remaining operating time between failure