P Brunotte

Bruno Enciu

M Peuportier

M Robillart

N El-Mankibi E

S Stathopoulos

Truchet

G Fraisse

B Souyri

F Wurtz

X Brunotte

P Enciu

B Peuportier

M El-Mankibi E

N Stathopoulos

S Truchet

E François

Shyam

Towards holistic building optimization using a computing environment that enable interoperability between numerical tools

Keywords: Global optimization, Surrogate model, Interoperability, Building, Life cycle Analysis

Towards holistic building optimization using a computing environment that enable interoperability between numerical tools

Introduction

The pronounced advances in computational techniques and optimization methods lead to applications of simulations for studying the complex engineering systems. Building performance optimization is a complex dynamic phenomenon where researcher generally used dynamic simulation model for the desired goals e.g. lowering energy consumption, reducing environmental impact, cost optimization, thermal comfort etc. [START_REF] Garber | Optimization stories: The impact of building information modelling on contemporary design practice[END_REF]. The decision parameters having the largest influence on performance correspond generally to early design phases, e.g. the architectural sketch including geometrical and structural choices. But other parameters defined in detailed design may be optimized as well. Environmental performance is evaluated using life cycle analysis (LCA) [START_REF] Polster | Evaluation of the environmental quality of buildings towards a more environmentally conscious design[END_REF], which integrates several indicators: primary energy consumption and CO 2 emissions, but also other criteria related to e.g. human health and biodiversity. Accounting for such a broad set of criteria is not common. Dynamic LCA [START_REF] Roux | Integrating climate change and energy mix scenarios in LCA of buildings and districts[END_REF] seems more appropriate than the standard static approach to evaluate impacts building on dynamic energy simulation results.

The main phases of building optimization studies are pre-processing (formulation of the optimization problem), running optimization (monitoring, control and error detection) and post processing (interpretation of the results) [START_REF] Nguyen | A review on simulation-based optimization methods applied to building performance analysis[END_REF]. Most of the building optimization researches used the single-objective approach [START_REF] Evins | A review of computational optimisation methods applied to sustainable building design[END_REF] but the real world building design optimization problems have opposite criteria simultaneously e.g. minimum energy consumption vs maximum thermal comfort; investment and operational cost etc. [START_REF] Hamdy | Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings[END_REF]. Therefore authors optimize energy systems and building envelop with multi-objective optimization. The best alternative solution were identified for least environmental impact of building using life cycle analysis principle considering the many economic and environmental criteria [START_REF] Antipova | Multi-objective optimization coupled with life cycle assessment for retrofitting buildings[END_REF]. Multi-objective optimization model was used by Carreras et al. [START_REF] Carreras | Multiobjective optimization of thermal modelled cubicles considering the total cost and life cycle environmental impact[END_REF] who simultaneously minimize the cost and the environmental impact linked with energy consumption in operational and construction phase. Using multi-objective optimization model Wu et al. [START_REF] Wu | Multi-objective optimisation of energy systems and building envelope retrofit in a residential community[END_REF] minimizes life cycle cost and greenhouse gas emission by simultaneous optimization of building energy system and retrofitting with heat pumps and renewable energy systems. With more than two objective functions Penna et al. [START_REF] Penna | Multi-objectives optimization of Energy Efficiency Measures in existing buildings[END_REF] considers energy saving, cost and thermal comfort as three objective functions to estimate the optimal total energy on a building including energy systems and envelope.

In building energy efficient design optimization the commonly used algorithms can be divided into three categories, viz. gradient-based search algorithms, evolutionary algorithms, and hybrid algorithms [START_REF] Terzidis | Algorithmic architecture[END_REF]. The computational optimization methods applied to sustainable building design were reviewed in [START_REF] Evins | A review of computational optimisation methods applied to sustainable building design[END_REF]. Machairas et al. [START_REF] Machairas | Algorithms for optimization of building design: A review[END_REF] presented the review about the algorithms used in performance-based building design optimization. However Attia et al. [START_REF] Attia | Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design[END_REF] addresses the gaps and needs for integrating building performance optimization tools in Nearly Zero Energy Building design.

The dynamic variables such as energy price, investment cost and climate change over the whole life span of building introduce the uncertainty. This leads to the robust optimization that increases the number of simulations to be performed and enhances the computational time. Therefore the aim of energy building design, now a day are to reduce the computational time using in particular surrogate model. Carreras et al. [START_REF] Carreras | Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling[END_REF] minimize cost and environmental impact using surrogate model based on a cubic spline interpolation. They showed that the implementation of surrogate model took around 1 day, which is approximately 8 times less as compared to the time consuming original SIMMOD model in Energy+ (more than 7 days).

A surrogate model includes majorly the sampling of input individuals and computes the reference model response to construct a data base for training the surrogate model, construction of surrogate model using suitable method e.g. radial basis function [START_REF] Bornatico | Surrogate modeling for the fast optimization of energy systems[END_REF], Kriging [START_REF] Hopfe | Robust multi-criteria design optimisation in building design[END_REF][START_REF] Tresidder | Acceleration of building design optimisation through the use of Kriging surrogate models[END_REF][START_REF] Eguía | Weather datasets generated using kriging techniques to calibrate building thermal simulations with TRNSYS[END_REF], support vector machine (SVM) [START_REF] Eisenhower | A methodology for meta-model based optimization in building energy models[END_REF], artificial neural network (ANN) [START_REF] Magnier | Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network[END_REF] and validation of surrogate model. In literature the Kriging model has been used with efficient global optimization (EGO) technique in single objective optimization problems [START_REF] Jones | A taxonomy of global optimization methods based on response surfaces[END_REF][START_REF] Ladevie | Analyses multicritères et méthode inverse en simulation énergétique du bâtiment[END_REF] as well as for multi-objective problems by generating a surrogate model for each objective function [START_REF] Koch | Statistical approximations for multidisciplinary design optimization: The problem of size[END_REF][START_REF] Wilson | Efficient Pareto frontier exploration using surrogate approximations[END_REF]. Panão et al. [START_REF] Panão | Optimization of the urban building efficiency potential for mid-latitude climates using a genetic algorithm approach[END_REF] presented a method based on genetic algorithm to optimize the urban forms for mid lattitude climates and concluded that lattitude dependent radiation conditions based optimization results in better thermal performance. The Artificial neural network (ANN) with multi-objective NSGA II genetic algorithm is used for optimization of thermal comfort and energy consumption in a residential house [START_REF] Magnier | Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network[END_REF]. ANN significantly reduces the optimization cost (3 weeks) compared to the the optimzation cost without ANN (10 years). A multivariative optimization using a nonlinear optimization scheme with building modeling based on physical, tecnhnical and economical interactions was presented in [START_REF] Peippo | Multivariate optimization of design trade-offs for solar low energy buildings[END_REF] to find the economic optimum. Kriging model is used in multi-objective optimization problems of many fields such as magnetics [START_REF] Berbecea | A Parallel Multiobjective Efficient Global Optimization: The Finite Element Method in Optimal Design and Model Development[END_REF], aerodynamic [START_REF] Baudoui | Optimisation robuste multiobjectifs par modèles de substitution[END_REF] and railways [START_REF] Berbecea | Multi-level approaches for optimal system design in railway applications[END_REF]. In building design optimization problems the Kriging model has been majorly utilized in multi-objective building design. A non-dominated sorting-based particle swarm optimization (NSPSO) algorithm together with the Kriging method with adaptive sampling procedure has been used to perform the multiobjective optimization of typical office room [START_REF] Li | Multi-objective optimization of HVAC system using NSPSO and Kriging algorithms-a case study[END_REF]. In this study, up to 46.6% reduction of computaional time has been reported. A recent study on building design optimization [START_REF] Wood | Using Kriging regression to improve the stability and diversity in NSGA-II[END_REF] reported that Kriging based fitness approximation slightly improve the NSGA II ; however authors in this study recommend that more research required to test this method for complex problems of building design optimization. Aijazi [START_REF] Aijazi | Machine Learning Paradigms for Building Energy Performance Simulations[END_REF] compared several common surrogate modeling techniques and reveal that parametric radial basis functions and Kriging are highly accurate regression techniques for predicting building energy consumption with about five orders of magnitude faster than the detailed Energy+ simulations. Authors conncluded that the impact of climate change on a building depends on its location and type.

In present optimization study, we have used the Kriging surrogate model for two cases namely (i) optimization using Camel and Rosenbrock test functions and (ii) a case study of building simulation using Energy+ software. The adaptive metamodel-based optimization approach has been considered by performing a series of multiple optimization processes using the Kriging model. The sampling process is done following design of experiment approach, and NSGA-II algorithm has been considered for the optimization process. We propose to implement the approach in an existing framework, called CADES dedicated to design and optimization [START_REF] Delinchant | An Optimizer using the Software Component Paradigm for the Optimization of Engineering Systems[END_REF]. This framework is based on a software component approach allowing the connection of models with optimization algorithms and pre or post-processing solution for setting, solving, and analyzing optimization problems. The approach is based on a software component approach offering a solution for inter-operability with the main simulation software of the field [START_REF] Gaaloul | Software Components For Dynamic Building Simulation[END_REF] and is compliant with the emerging software component standard FMU 1 . The originality of this environment is to offer a proposal of integrating, in the same environment, inter-operability, multi-objective optimization and connection with Kriging surrogate models and parallel computing. The goal is to simplify the use of those technics, so that they can be used by final users in design offices.

Methodology

Kriging model

The Kriging method is defined as optimal interpolation based on the regression against observed values of surrounding data points, weighted according to spatial covariance values. The random field of Kriging is composed of residual and trend components. The residual component has a stationary mean of zero and has a constant covariance [START_REF] Krige | A statistical approach to some basic mine valuation problems on the Witwatersrand[END_REF]. There are three Kriging types namely the Simple Kriging (trend component is constant over entire domain and mean is known); the Ordinary Kriging (the trend component is constant in the local neighbourhood of each estimation point and mean is unknown); and Universal Kriging (composed of non-random trend function and a real valued residual function).

The optimization tool has been developed in CADES following Openturns documentation. The set of initial sample input data (individuals) with uniform distribution between minimum and maximum bound is generated following random weighted design of experiment with latin hypercube sampling (LHS) technique. The Kriging model approximates the reference model using the array of input (learning) individuals and produces the array of learning performance (output) corresponding to the learning individuals. We compute the metamodel on array of learning individuals and compute the relative errors on learning points. The covariance between two samples

  n R t s  , is defined as following             t t s t m X s m X E t s Cov    . , (1)
To obtain the covariance matrix, we discretize the stationary covariance function with 50x50 meshes. In optimization with NSGA II, we minimize the covariance for the accurate prediction from the Kriging metamodel.

The output of Kriging metamodel is Gaussian random vector with stationary covariance function defined as following

      x Z x f x Y T    . ( 2
)
Where   

        x x C x Z x Z E stat s . ~2     (3)
This output is given as the input argument for the calculation of Gaussian random vector at new point.

The adaptive design of experiment (DoE)

The adaptive design of experiment allows to both fitting the Kriging model in the global space and near the optimal individuals (Pareto frontier). The schematic for the methodology is given in Fig. 1.

Fig. 1. Methodology adopted for the optimization using Kriging metamodel

The algorithm starts by sampling the initial data points using the Latin Hypercube Sampling technique (LHS). The set of points is then evaluated using the reference time consuming model. The Kriging surrogate model is identified for all objective functions. In step 1, the individual with maximum covariance is selected using NSGAII algorithm and added to the data base. The aim of step I is to explore the whole (global) design space. In step 2, the identified Kriging model is then trained to run the multi-criteria optimization process using also NSGA-II algorithm to find the optimal solutions (Pareto frontier). The new individual with maximum covariance in the Pareto frontier is added to the data base for further iteration in the Kriging model identification. The optimization process ends with the maximum number of loops not greater than the sum of number of individuals in each steps (N=N LHS + N global + N pareto ) or if the minimum error target is achieved.

Implementation of the Design of Experiment in a user friendly environment

The previous described DoE methodology has been implemented and integrated in the CADES framework [START_REF] Delinchant | An Optimizer using the Software Component Paradigm for the Optimization of Engineering Systems[END_REF]. Fig. 2 gives a view of how a connection can be easily made with a TRNSYS model, demonstrating the interoperability possibilities implemented that must be simple as possible for the final designer in the design office (toward a "plug and play" approach). Similar solutions will be available for connection to Energy+ model, or FMU software components.

Fig. 2 Connection with a TRNSYS Black-Box model (Time consuming model) -Inter-operability in the proposed tool

In DoE approach, initial individuals correspond to the number of individuals (N LHS ) that are allowed to be evaluated in the first LHS sampling of the design space. The number of points for surface improvement (N global ) corresponds to the maximum number of individuals that can be tested on the time consuming model in the first improvement loop of the methodology, when new sample are added for minimizing the maximum covariance of the Kriging surface (step I of the methodology). The number of points for Pareto improvement (N pareto ) corresponds to the number of individuals that can be tested on the time consuming model in the second improvement loop of the methodology when new samples are added to improve the Pareto front of the defined optimization problem (step II of the methodology). This allows controlling the maximum number of calls for the time consuming model, which cannot be greater than the sum of the 3 previous parameters N=N LHS + N global + N pareto . The user must also define two additional parameters: "Surface acceptable relative error" corresponds to the required error in step I: if the error is lower than this value, for the point having the maximum covariance, the sampling on the time consuming models stops for step I. "Pareto acceptable relative error" corresponds to the required error in step II: if the error is lower than this value, for the point having the maximum covariance, the sampling on the time consuming models stops for step II. For reaching this, a connection has been made with the Kriging library implemented in Openturns2 . An option allows also parallelizing the computing on the time consuming models on multi-core computers (not presented in this paper).

Results and discussion

In this section we present the results for the test functions and for the building case study.

Case study n°1: test functions

The Camel and Rosenbrock functions are selected as test function for the present multi-objective optimization study. We know the Pareto frontier of these test functions and also these reference test functions are fast. Both test functions are minimized in the interval (-2, 2) for 1 x and 2 x (decision parameters). The acceptable relative errors in step I and step II are kept equal to 10 -4 and 10 -5 respectively.

 Camel and Rosenbrock functions

The camel and Rosenbrock functions are given by Eq. ( 3) and ( 4) respectively

    1 4 3 1 . 2 4 , 2 2 2 2 2 1 2 1 4 1 2 1 2 1 1               x x x x x x x x x f (3)       2 2 1 2 2 1 2 1 2 * 0 . 100 1 , x x x x x f     (4)
 Responses surfaces at the end of the design of experiment The response surfaces of reference model and Kriging model along with the number of individuals in LHS phase, after step I (global phase) and after step II (Pareto phase) for camel and Rosenbrock functions is shown in Fig. 3.

Fig. 3. Surfaces for test functions and Kriging model after step II

These surfaces have been obtained for N=75 variables distributed as 25 individuals in each step. It can be observed from these surfaces that the individuals are distributed over whole space in LHS phase and step I (with many points on the boundaries for step I) but during step II the individuals are near to Pareto frontier.

 Comparison of the Pareto frontiers in the design or objective spaces

The Pareto frontier in design space and objective space for reference and after each step are obtained and depicted in Fig. 4. It can be observed that the Pareto frontiers after step I is far from the reference function but after step II the Pareto frontiers is closer to the reference Pareto. To assess the accuracy of the metamodel, we calculate the R 2 global (step I) values with a 50x50 mesh for design variables 1

x and 2

x . The values product of R 2 for camel and Rosenbrock functions are calculated for both global space (R 2 global,ros .R 2 global,cam ) and individuals of the Pareto frontier (R 2 pareto,ros .R 2 pareto,cam ). As we increase the number of individuals N, the R 2 product approaches to 1 indicating the accuracy of the metamodel with respect to reference for both the global space and the Pareto area (Fig. 5(a) and 5(b)). A number of individuals, N=75 is the best choice for our case study (compared with N=45/60).

Case study n°2: building

The building studied has four levels. It has a living area of about 1200m² (Fig. 6). The building is composed of 17 apartments, a landing, a garage / crawl space and attic. Each of these areas has been simulated as a thermal zone. The model has 20 thermal zones in total. It is located in Chambery (Savoie, France) at an altitude of 235m.  Decision parameters and objective functions In order to optimize the building, decision parameters and objective functions have been defined. The decision parameters used to test the tool and their range of variation are presented in Table 1.

Table 1. Decision parameters

             z e
where, x is insulation thickness (m), y is concrete slab thickness (m) and z is w

U ( K m W 2 )
The objective is to find the optimum between the sum of heating and cooling loads which must be the lowest possible, and the cost of the operation which must also be minimized.

 Results

We run the optimization with N=75 individuals as in case of test functions but the Pareto frontier obtained after step II was not closer to the reference Pareto frontier (obtained directly with NSGA II and Energy+). This may be due to the 4 decision parameters instead of 2 decision parameters for test functions. Therefore we run the optimization for 105 individuals. The Pareto frontiers for reference model and after each step are given in Fig. 7. It can be observe that the Pareto frontier is not smooth as the case of test function which reflects the complexity of the case study problem which can be credited to increased number of decision parameters. In this work, our main objective was to validate the approach developed in CADES. Of course further work is necessary to precisely analyze the optimal solution as in [START_REF] Gilles | Decision-making based on network visualization applied to building life cycle optimization[END_REF]

Conclusions and outlooks

A global strategy for making a Design of Experiment on time consuming models has been proposed in this paper. The deployment of such an approach can be a complex task for final users in design offices. That is why the approach has been implemented in a framework in which the complex computer science aspects, like interoperability or connection with the Kriging libraries is encapsulated. The idea is to offer, for the final user, an approach as closed as possible from a "plug&play" approach. The perspective is now, to work on tutorials and illustrations of the use of the methodology and the software on real examples. By this way, we hope that future users will not only be able to use the approach, but also to understand, how they must configure and use it to go towards an efficient design process. For the test functions we observe that, with increasing number of individuals the R 2 values approaches to 1 which justify the accuracy of the Kriging model. The number of individuals which gives the R 2 value near unity depends on the complexity of the reference function. As observed from the case study n°2, with increased decision parameters, the number of individuals N needed to predict the model accurately is increased (from N=75 to 105). Therefore further study is required with different test functions and increasing complexity to find the minimum number of individuals to predict the reference accurately, particularly in the Pareto area. In this first step, LCA has been performed in a separate computer platform including also energy simulation and optimization [START_REF] Recht | Ecodesign of a 'plus-energy' house using stochastic occupancy model, life-cycle assessment and multi-objective optimisation[END_REF]. The considered decision parameters were the insulation thickness in walls, roofs and floors, glazing type (double or triple), ventilation system (with or without heat recovery). According to optimization results shown in Fig. 8, a limited supplementary cost of 40 € per m 2 floor area would allow CO 2 emissions, over the life cycle of the building, to be reduced by 23% compared to the base case.

Fig. 4 .

 4 Fig. 4. Pareto frontier reference and after each step a) with decision parameters (x 1 , x 2 ) and b) with objective functions (f 1 , f 2 ) Kriging model accuracy The coefficient of determination R² is calculated between the individuals for reference model and the individuals obtained with the metamodel. R 2 value has been calculated for each new individual obtained in steps I and II.

Fig. 5 .

 5 Fig. 5. Variation of a) R 2 global and b) R 2 pareto with number N=45/60/75 during step I and II

Fig. 6

 6 Fig. 6 South and East views of the building "Les Roches Blanches"The buildings walls, intermediate floor, partitions and floor are made of concrete. Walls have gypsum plaster board and exterior insulation. Floor on crawl space has Polystyrene insulation; however the Flat roof and Floor under attic have polyurethane insulation and glass wool insulation respectively. The windows in rooms are PVC double glazed windows with overall heat transfer coefficient U w = 1.3 W/(m².K). The number of occupants in an apartment kept equal to the number of room in the apartment. The occupancy for different hours during weekdays and weekend are varied from 25% -50% -100%. The internal heat gains of housing (related to cooking, electrical appliances, etc.) are taken into account on hourly basis and vary between 1.5 W/m² to 7.68 W/m². The cooling and natural ventilations have been taken into account from 15 July to 15 August. The infiltration was considered 0.15vol.h -1 . The mechanical ventilation varies with size of the apartment. Dynamic thermal simulations were run in Energy+ 8.5 software. This software models the thermal comfort, ventilation, infiltrations, internal heat gains, and the heating and cooling loads of the building. The weather file (Meteonorm) used is a typical year weather of the city of Chambéry (Savoie, France).

-

  thickness of insulation in the exterior walls 0.1 to 0.4m 906 The thickness of the slab of the intermediate floor 0.1 to 0.25m 869 The thickness of insulation in the floor on the outside 0.1 to 0.4m 216 The thickness of insulation in the flat roof 0functions used to evaluate the decision parameters are the sum of heating and cooling loads supplied by the system to the building and the cost related to the changes. The heating and cooling loads are directly calculated by Energy+. They represent the energy demand of the building. The cost equations include the price of the material and the installation. Costs are expressed in €/m Insulation thickness variation cost of the floor on the outside

  For the reference model with NSGA II, 1321 simulation are required to run the optimization. The time needed for one simulation is approximately 7.5 minutes. Therefore the time required to run the optimization with Energy+ is approximately 7 days. With Kriging model we have run the optimization with 105 individuals, therefore the time required to run the optimization with Kriging model is 13 hours which is approximately 13 times faster.

Fig. 7

 7 Fig. 7 Pareto frontier for energy need and total price of insulation for the building (case study)

Fig. 8

 8 Fig. 8 Pareto frontier for CO 2 emissions and construction cost (case study)

See also following reference for description of the software component approach used for inter-operability http://muse-component.org/

See : http://www.openturns.org/

Acknowledgments

This study has been supported and funded by the French National Research Agency (ANR), under grant agreement N° ANR-13-VBDU-0003-01, Acronym INTENSE. Partners: LOCIE, LGCB, ARMINES, G2ELAB, CEA-INES, ALBEDO, FAURE, VESTA SYSTEM


Pierre Granjon
email: pierre.granjon@grenoble-inp.fr

Arshpreet Pedro Dias Longhitano

Pedro Dias

Arshpreet Sing

Arshpreet Singh

Comparison of electrical and vibration analysis methods for mechanical fault monitoring in wind turbine drivetrains

come

Introduction

Onshore or offshore wind turbines are still a developing technology. Though their reliability has improved in time, it can be further increased by implementing efficient condition monitoring systems and predictive maintenance strategies. Currently, vibration analysis seems to be the most popular condition monitoring technique for the mechanical parts of their drive train, such as gears or bearings. However, other possibilities exist, such as analyzing the electrical quantities generated by the output electrical generator driven by the drive train. For example, a method has been recently proposed in [START_REF] Cablea | Three-phase electrical signals analysis for mechanical faults monitoring in rotating machine systems[END_REF] to detect mechanical faults in electrical rotating machines using the whole information contained in three-phase electrical currents. Such an electrical approach led to very encouraging results when applied on a testbench emulating a wind turbine. Consequently, the goal of this paper is to apply the same strategy on an industrial wind turbine and check if in this case the same positive results can be obtained. For this, the next section of this paper describes the monitored wind turbine and the available database, while the fault to be detected and its vibratory and electrical signatures are detailed in section 3. Section 4 explains the data selection process applied to the whole database, and the results obtained by using vibration and electrical data are described and compared in section 5 and 6. Finally, the last section of this paper presents the overall conclusions of this research work.

Process and measurements

The process of interest is an industrial onshore wind turbine installed in the wind farm of Arfons-Sor located in France (see Fig. 1) and owned by Valemo. This wind farm is constituted by eleven wind turbines of type Eco 80 2.0 from ALSTOM Ecotecnia (see Fig. 2.a) with the same characteristics: 2MW rated power, 80m rotor diameter, 70m hub height. The main components of their drive train are given in Fig. 2.b, and consist in a main bearing supporting the low-speed shaft driven by the hub, a planetary gearbox followed by a 2-stage parallel gearbox, and the high-speed shaft driving a three-phase induction generator. In the context of the European project KAStrion presented in [START_REF] Martin | KAStrion project: a new concept for the condition monitoring of wind turbines[END_REF], two of these wind turbines (WT6 and WT8 in Fig. 1.b) were instrumented at the end of 2014 with an acquisition system and several mechanical and electrical sensors. Six accelerometers were mounted on different bearings of the drive train. A phase marker sensor was installed on the high-speed shaft for order-domain analysis and eventual angular resampling. Concerning electrical quantities, three current sensors and three voltage sensors were mounted on the stator of the electrical generator. This set of sensors gives access to different physical quantities which were regularly acquired (at least once a day) for monitoring purpose during the beginning of 2015 with the following acquisition parameters:

• sampling frequency = 25kHz,

• length of vibration signals = 40s,

• length of electrical signals = 10s. In what follows, only one part of this large database is used to compare electrical and vibration analysis methods for mechanical faults monitoring:

• one vibration signal measured at the input bearing of the planetary gearbox,

• the three phase currents and line voltages of the stator of the electrical generator,

• the phase marker signal.

Fault signature

On the 30 th of December 2015, a total breakdown of wind turbine WT8 occurred. After inspection, a major mechanical fault located in the main bearing was diagnosed. More precisely, it was evident that an inner race fault continuously developed during a long period of time to finally end in the bearing partial destruction and locking.

In order to compare the detection capabilities of approaches based on electrical or vibration signals applied to this kind of fault, the signature of this fault in vibrations and electrical quantities must first be described.

Fault signature in vibration data

Inner race bearing faults generate mechanical impacts with a frequency [START_REF] Randall | Rolling element bearing diagnostics -a tutorial[END_REF]:

f BPFI = N 2 f R 1+ d B d P cos β         , ( 1
)
where N is the number of rolling elements, f R < f BPFI is the shaft rotating frequency, d B and d P the ball and pitch diameters, and β the contact angle.

In a 1 st approximation [START_REF] Mcfadden | Model for the vibration produced by a single point defect in a rolling element bearing[END_REF], these impacts can be assumed purely periodic with a fundamental frequency f BPFI , and amplitude modulated at the shaft rotating frequency f R by the load effect. This is not the case in more refined models [11,12] where the contact angle depends on the ball position, leading to mechanical impacts containing also a random part. In both cases however, their low-frequency content is mainly constituted by frequency peaks with frequencies harmonically related to f BPFI surrounded by sidebands spaced by f R . These impacts generate mechanical waves 4 travelling through structures to the sensor where the vibration signal is finally measured.

Even by taking into account the time variation of the transfer function between the impact location and the sensor, the low-frequency content of the vibration signal due to this particular fault can be approximated by a series of peaks of frequencies

k × f BPFI ± l × f R
where k and l are integers and f BPFI > f R . These set of peaks finally constitutes one part of the vibratory signature of inner race bearing faults. Table 1 gives the values of these characteristic frequencies for the main bearing of the monitored wind turbine. They are expressed in orders of the high-speed shaft rotating frequency.

f BPFI 0.31605 ball pass frequency of inner race f R 0.00995 low-speed shaft rotating frequency Table 1. Characteristic frequencies of the faulty main bearing expressed in orders of the high-speed shaft rotating frequency [START_REF] Firla | Automatic signal processing for wind turbine condition monitoring[END_REF]

Fault signature in electrical data

The mechanical impacts previously described have no effect on electrical quantities measured at the output of the wind turbine generator. However, bearing faults also generate load torque oscillations on the rotating shaft where the faulty bearing is located, which is the low-speed shaft in the present case. These torque variations occur at the same rate as the mechanical impacts and have the same characteristics. Therefore, they can also be approximated in the low-frequency range as a series of peaks with frequencies k × f BPFI ± l × f R (k and l integers). If these torque oscillations reach the high-speed shaft by travelling through the drive train, they will affect the electrical generator and consequently its electrical quantities. In [START_REF] Blödt | Models for bearing damage detection in induction motors using stator current monitoring[END_REF][START_REF] Blödt | Mechanical fault detection in induction motor drives through stator current monitoring -theory and application examples[END_REF], such torque oscillations have been shown to cause amplitude and/or phase modulations in electrical quantities and more particularly in stator currents of induction machines. One part of the electrical signature of an inner race bearing fault is therefore the presence of amplitude and/or phase modulations of frequencies k × f BPFI ± l × f R (k and l integers) in the stator currents of the output generator of the monitored wind turbine.

Data selection process

As mentioned in section 2, electrical and vibration data were regularly (at least daily) acquired on the monitored wind turbine from January to May 2015, enabling the constitution of a large database. The first task to do is then to select a correct data subset from the whole database enabling to simply and correctly monitor the wind turbine. In order to simplify as much as possible the analysis methods used to monitor the bearing fault, the first criterion is to select signals which are as stationary as possible. This requirement is more or less equivalent to select the signals acquired with a shaft rotating frequency as constant as possible. Therefore, the variations of the shaft rotating frequency during each acquisition constitutes a first interesting feature to select the data. In order to easily compare the results obtained for different signals, the data should be acquired when the monitored wind turbine runs around the same operating point. This requirement is more or less equivalent to select the data acquired for similar shaft 5 rotating frequency and electrical power generated. Therefore, mean values of the shaft rotating frequency and of the electrical power computed over the acquisition time constitute other interesting features to select the data. From this and noting that vibration and electrical data were not acquired at the same time, the two following data selection strategies have been developed.

Vibration data selection

From the previous discussion, the useful features to select vibration data are the mean value of the shaft rotating frequency (representing the wind turbine operating point) and its maximum variation (representing the stationarity of signals) computed over the acquisition time. Therefore, for each acquired vibration signal, the following quantities are determined:

• The time evolution of the high-speed shaft rotating frequency is estimated thanks to the phase marker signal. This sensor gives one phase reference per round of the high-speed shaft, such that one estimation f H n     of its rotating frequency is realized for each round n.

• The mean value f H of f H n   
 is then estimated over the whole acquisition time.

• In order to measure the variations of f H n     , several quantities can be estimated. The values of f H are clearly concentrated close to 30Hz, which is the nominal value of the electrical generator rotating frequency. The distributions of σ H and ∆ H show that the major part of their values stay close to 0, whatever the acquired signal. Finally, data verifying f H > 29.5Hz (the wind turbine works close to its nominal operating point) and σ H < 1% and ∆ H < 5% (the rotating frequency is almost constant and the signals almost stationary) are selected in the final dataset. The corresponding thresholds appear as red lines in Fig. 3.a, and finally lead to a set of 42 selected vibration signals. The distribution of these selected signals over time given in Fig. 3.b shows that the obtained vibratory database correctly covers the period of time during which the wind turbine is monitored (from January to May).

Electrical data selection

The electrical data selection strategy is exactly the same as for vibration data, but an additional criterion is used on the electrical quantities generated by the wind turbine generator to further specify its operating point. The idea is to keep electrical data sufficiently close to their maximal and nominal value. This can be done for example by computing the RMS value of each acquired electrical data, and discarding those for which this value is too small compared to its maximum. The electric data are finally selected if they also verify the previous criteria concerning the shaft rotating frequency. This selection strategy is applied to the whole electrical database consisting of 790 records, and leads to a set of only 25 electrical data. Fig. 4.a compares the whole electrical dataset (in blue) and the selected one (in red). In this figure, each data is represented as a point in a plane with f H on the abscissa and the electric power (roughly estimated as the product of current and voltage RMS values) on the ordinate. Such a curve is quite similar to the power curve usually used to characterize the operating points of wind turbines. This figure clearly shows that every selected data was acquired when the wind turbine ran around the same operating point obtained for the nominal shaft rotating frequency and the nominal electrical power. Moreover, their time distribution represented in Fig. 4.b shows that the selected electrical database correctly covers the desired period of time going from January to May. 7

Vibration data results

According to section 3.1, one part of the vibratory signature of an inner race bearing fault is constituted by a set of spectral peaks with frequencies k × f BPFI ± l × f R (k and l integers), where f BPFI is given by Eq. ( 1) and f R < f BPFI is the low-speed shaft rotating frequency. The values of these frequencies expressed in orders of the high-speed shaft rotating frequency f H are given in Table 1.

The presence and the temporal evolution of this signature can be easily studied by computing the power spectral density of the selected vibration signals in the frequency band of interest. The chosen estimator is a simple averaged modified periodogram or Welch's periodogram [START_REF] Welch | The use of the fast Fourier transform for the estiation of power spectra: a method based on time averaging over short modified periodograms[END_REF], with a Blackman window of length 2.6s (corresponding to a spectral resolution of 0.02 orders of f H ), an overlap of 75% and a zero-padding factor of 4. The obtained results are given in Fig. 6 where the PSDs obtained at the beginning of the period of interest (January) appear in blue and the ones obtained at the end (May) appear in red. The top figure shows a frequency band were the first 3 harmonics of f BPFI labelled by black arrows are visible, contrarily to their sidebands hidden by a strong wide band noise and the limited spectral resolution. Clearly, the fault signature is already present in the first PSDs, but not with a significant level. Its amplitude is much more important in the last PSDs, highlighting a large increase in the fault signature between January and May. The down figure focuses on the 1 st harmonic of f BPFI , which is further used to compute a simple fault indicator obtained by summing the estimated PSDs around this particular frequency (more precisely between 0.29 to 0.34). Fig. 6 shows the temporal evolution of this indicator during the whole period of interest according to the measurement number (Fig. 6.a) and the measurement date (Fig. 6.b). A large increase of this quantity between the end of March and the end of April is clearly visible in these figures. Therefore, this simple vibration fault indicator shows that the condition of the main bearing inner race strongly deteriorates during the period of interest, and particularly during the month of April.

6.a evolution vs. measurement number 6.b evolution vs. measurement date Figure 6. Fault indicator obtained through vibration data

Electrical data results

According to section 3.2, one part of the electrical signature of the inner race bearing fault is the presence of amplitude and/or phase modulations of frequencies k × f BPFI ± l × f R (k and l integers) in the stator currents measured at the output generator of the monitored wind turbine. A logical approach is then to demodulate the main component of these currents, in other words their fundamental component located at f 0 = 50Hz, and check the presence of components of frequency k × f BPFI ± l × f R in the demodulated quantities. A three-phase method was recently proposed in [START_REF] Cablea | Three-phase electrical signals analysis for mechanical faults monitoring in rotating machine systems[END_REF] to realize such a three-phase demodulation, jointly with a fault indicator dedicated to mechanical faults in threephase electrical rotating machines. The whole structure of this method is summarized in Fig. 7, where three main parts clearly appear. The first step of this algorithm is the application of the instantaneous symmetrical component (ISC) transform [START_REF] Paap | Symmetrical components in the time domain and their application to power network calculations[END_REF] to the three phase currents. It consists of a simple matrix product between the three measured stator currents and the inverse Fortescue matrix. This operation returns the three ISCs corresponding to the measured currents. The second step consists in applying a Hilbert demodulation technique [START_REF] Reilly | Analytic signal generation -tips and traps[END_REF] to the positive sequence ISC (noted i + t ( ) in Fig. 7) around the electrical fundamental frequency f 0 = 50Hz. As explained in [7], the output of this second step is the instantaneous amplitude and frequency of the electrical fundamental component (noted a + t ( ) and f + t ( ) in Fig. 7). Once the three-phase quantities are demodulated, the third and last step is the fault indicator computation based on the integration of a simple power spectral density estimator. The two main advantages of using this three-phase approach compared to usual single-phase algorithms is that it uses the whole information contained in three-phase electrical quantities, and it improves the final signal-to-noise ratio in the demodulated quantities. As a direct consequence, the fault indicators proposed in Fig. 7 offer better detection capabilities than the ones based on single-phase approaches [START_REF] Cablea | Three-phase electrical signals analysis for mechanical faults monitoring in rotating machine systems[END_REF]. This approach is applied to the three-phase currents belonging to the selected electrical dataset described in section 4. Fig. 8 shows the spectral content of the demodulated electrical quantities, in other words of the instantaneaous amplitude a + t ( ) (Fig. 8.a - top) and frequency f + t ( ) (Fig. 8.a -down) of the fundamental component located at 50Hz. These power spectral densities have been estimated with the same estimator as for vibration data. No clear modulating component increases in the spectrum of a + t ( ) at the expected frequency of f BPFI labelled by black arrows. This shows that this quantity contains few information on such a bearing fault. On the contrary, this particular component increases in the spectrum of f + t ( ) during the period of interest, as highlighted by the focus of Fig. 8.b. Consequently, a simple fault indicator can be obtained by summing the power spectral density of f + t ( ) around the expected fault frequency f BPFI (and as for vibration data, between 0.29 and 0.34).

b). A slight increase of this quantity between the end of March and the end of

April is visible in these two sub-figures. When comparing these results with the ones of Fig. 6, it is clear that the electrical indicator is less sensitive to the inner race fault than the mechanical indicator. 9.a evolution vs. measurement number 9.b evolution vs. measurement date Figure 9. Fault indicator obtained through electrical data However, An important point to notice is the complexity of the wind turbine drive train, composed of a planetary gearbox followed by a 2-stage parallel gearbox. Indeed, the accelerometer delivering the signal used to compute the mechanical indicator is located close to the main bearing and finally to the fault impacts. On the contrary, in order to influence the electrical generator, the load torque oscillations due to the main bearing fault must propagate through all the elements of the gearboxe to the high-speed shaft. As a consequence, their electrical signature in the stator currents is less important, and the fault detection is more difficult.

Conclusions

This paper compares electrical and vibratory condition monitoring methods to detect a mechanical fault occurring in the main bearing of an industrial wind turbine. Firstly, a data selection process uses the shaft rotating frequency and the electrical power at the output of the wind turbine in order to ensure that the processed data are stationary and acquired around the same operating point. Secondly, vibration and electrical data are processed on the same period of time to obtain two different mechanical fault indicators dedicated to inner race bearing faults. Clearly, these two indicators behave similarly and increase during the same month, indicating that an inner race fault probably developed in the wind turbine main bearing during this particular month. However, the indicator obtained through vibrations seems to be more sensitive than the one relying on electrical data. As mentioned in section 6, this is due to the fact that the fault is distant from the electrical generator located on the other side of the drive train. In such a case, the electrical indicator can be used to confirm the presence of a mechanical fault, but hardly to detect it with certainty.

  Figure2. Arfons-Sor wind turbines[START_REF] Firla | Automatic signal processing for wind turbine condition monitoring[END_REF]

Figure 3 .

 3 Fig. 3.a.

  4.a data selection criteria 4.b time distribution of selected data Figure 4. Electrical data selection

Figure 5 .

 5 Figure 5. Power spectral densities of vibration signals -frequency in orders of f H (blue = January, red = May)

Figure 7 .

 7 Figure 7. Mechanical fault indicator computation for three-phase electrical data

Figure 8 .

 8 Figure 8. Power spectral densities of demodulated electrical quantitiesfrequency in orders of f H (blue = January, red = May)


Othmane Belmoukadam

Thierry Spetebroot

Chadi Barakat

ACQUA: A user friendly platform for lightweight network monitoring and QoE forecasting

ACQUA, Application for prediCting QUality of Experience at Internet Access, is an Android application to measure mobile network access performance and to estimate how this performance affects main applications of interest to the end user from Quality of Experience (QoE) viewpoint. Every application supported in ACQUA is a function, or a model, built offline, that links the network-level and device-level measurements to expected Quality of Experience. Supervised machine learning techniques are used to establish such link. Thanks to a lightweight measurement strategy, ACQUA can perform measurements continuously and show to the user, in addition to QoE estimations, measurement results obtained both actively and passively. This way we we can provide insights over time on network performance as well as user-friendly QoE values that explain the state of the network. Finally, ACQUA provides a feedback channel for end users to express themselves about their real QoE, a feedback that we plan to use to assess the importance of the subjectivity of QoE in real life scenarios.

I. INTRODUCTION

Monitoring mobile networks, mainly cellular ones, has been the subject of a lot of research and a variety of tools in the recent period. Available tools cover a large spectrum of aspects such as network performance related issues (e.g., Speedtest [START_REF]Ookla. speedtest.net[END_REF] and MobiPerf [START_REF] Huang | Mobiperf: Mobile network measurement system[END_REF]) and middlebox related issues (e.g., Netalyzr [START_REF] Kreibich | Netalyzr: illuminating the edge network[END_REF]). These tools shed light on important aspects of modern mobile networks and the way they perform and are administrated. Yet, these tools still focus on the network itself and deliver few information to the end user regarding his/her Quality of Experience (QoE) as perceived, or going to be perceived, when using the mobile network services and applications. By definition, QoE is a subjective measure determined by several subjective and objective factors. It models the user perception of the service delivered by the network for a specific application or as a whole. In this work, we are mainly concerned by the impact of the network access performance on the QoE (often expressed on a scale from 1 to 5, called Mean Opinion Score or MOS in brief).

The literature is rich with research-driven measurement tools specialized in some aspects of Quality of Experience such as YoMoApp for YouTube [START_REF] Wamser | Yomoapp: A tool for analyzing qoe of youtube http adaptive streaming in mobile networks[END_REF] and QoE Doctor [START_REF] Chen | Qoe doctor: Diagnosing mobile app qoe with automated ui control and cross-layer analysis[END_REF] for device performance issues and their impact on the QoE. However, there is still a need for a general lightweight tool providing forecasts of end user Quality of Experience at the application level, together with periodic and extensive measurements of network access performance. On one side, this tool would help end users better understand the performance they are getting from their network access and how it relates to their expected QoE. On the other side, it would allow network operators and content providers to troubleshoot access network performance issues, and better design their systems so that Quality of Experience of their customers is optimized.

For this aim, we developed ACQUA, an android application based on network and device level measurements. ACQUA deploys machine learning models built offline in a controlled network environment, to map measured network Quality of Service (QoS) to estimated Quality of Experience for different applications of interest to the user. ACQUA is meant to be a cross-application QoE estimation tool. It is mainly concerned with access performance issues and implements techniques to measure this performance and interpret it in terms of QoE, thanks to models calibrated offline. The performance of the access is supposed to be measured towards servers that we deploy close to the end user to capture the access and only the access independently of any network core issues. Currently, and for the sake of proving the concept, we deploy these servers in our laboratory and make sure they are well connected to the well dimensioned French academic backbone network Renater. With this approach, we aim at making network measurements reused by different QoE models and allowing ACQUA to provide estimations of the QoE without running the applications themselves, hence reducing the overhead on the mobile. The way we build these models and their accuracy have been discussed in two of our recent papers for two popular applications, Skype [START_REF] Spetebroot | From network-level measurements to expected quality of experience: The skype use case[END_REF] and YouTube [START_REF] Khokhar | On active sampling of controlled experiments for qoe modeling[END_REF].

ACQUA is meant to be lightweight and able to perform periodic measurements at a granularity up to one measurement session per minute (Sec. III). With existing tools, this is almost impossible given the data consumption implicated. ACQUA implements UDP active measurement techniques to probe the network faster and with fewer packets and is thus able to capture network performance (e.g., delay, bandwidth and loss rate) with tens of Kbytes per measurement session, without compromising measurement accuracy. To the best of our knowledge, ACQUA is the only tool able to perform periodic fine grained measurements of mobile access performance along with estimation of QoE for daily used applications.

As said above, the models of QoE in ACQUA are built offline with the help of controlled experimentation [START_REF] Spetebroot | From network-level measurements to expected quality of experience: The skype use case[END_REF] [START_REF] Khokhar | On active sampling of controlled experiments for qoe modeling[END_REF]. The network conditions are artificially tuned using network emulation tools, applications are played out, and the corresponding QoE is written down. Supervised Machine Learning is used to build models relating QoE to network performance. These models are then pushed into the mobiles via the ACQUA application. It is clear that QoE annotation is an important step in our approach. For this, we follow an expert model approach which consists in reusing existing models in the literature or in the standards to produce the QoE annotation. To be noted here that our models differ from existing models by the fact that ours (only) consider a rich set of network performance metrics as inputs (i.e., no application level QoS), and so they overpass the details of the transport and the application layer. Now, to assess how real perceived Quality of Experience differs from our estimation, and also to validate how well existing models for QoE annotation perform in real life scenarios, we solicit the end user in ACQUA for her/his real Quality of Experience. We also collect in parallel network and device level measurements performed at the moment of the feedback. We plan to use this crowdsourced data to understand the subjectivity of the QoE in real life scenarios to evaluate the exact impact of the network performance on it.

In this paper, we describe our tool ACQUA and the key ideas behind it. We first go via the state of the art and briefly overview the related work to ACQUA. In the section that follows, we explain our platform and dig into some technical issues we faced during the development of ACQUA, and then present validation results for the network active measurement techniques deployed by our tool. Later, and as a first step towards using ACQUA data for QoE troubleshooting, we present descriptive and inferential statistics and point to passive metrics collected inside the mobile that might be related to QoE degradation. Before concluding, we present some limitations of our platform and our plans for future work.

II. RELATED WORK

In this section, we summarize the related work to ACQUA on the two aspects of QoE modeling and mobile sensing.

A. QoE modeling

When addressing Quality of Experience, modern standards (e.g., ITU) focus on protocols or general class of applications and not on specific Internet services. Among these, most target application level metrics, such as the number of interruptions or the number of bitrate switches during a video playback [START_REF] Robitza | HTTP Adaptive Streaming QoE Estimation with ITU-T Rec. P.1203 Open Databases and Software[END_REF], [START_REF] Raake | A bitstream-based, scalable video-quality model for HTTP adaptive streaming[END_REF] or the signal degradation within VoIP services [START_REF]Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs[END_REF]. For what concerns the network aspects and how they affect the user Quality of Experience, standards give general recommendations (e.g., consider only one parameter or define minimum thresholds to have service). While being suitable for predicting users' QoE, those expert models developed in the standards are too generic when addressing network inputs or too specific when addressing application-level QoS metrics (i.e., inputs cannot be reused for different services).

On the research side, different approaches exist that target specific services or aspects of QoE. Among them, QoE Doc-tor [START_REF] Chen | Qoe doctor: Diagnosing mobile app qoe with automated ui control and cross-layer analysis[END_REF] addresses device performance issues and their impact on the QoE, while Hoßfeld et al. studies how Youtube QoE is affected by application level measurements [START_REF] Hoßfeld | Quantification of youtube qoe via crowdsourcing[END_REF]. YoMoApp [START_REF] Wamser | Yomoapp: A tool for analyzing qoe of youtube http adaptive streaming in mobile networks[END_REF], a video streaming crowdsourcing application, targets the collection of Youtube's QoS metrics and users' feedback.

Popular mobile applications do not embed Quality of Experience feedback related to the network conditions, with the exception of some applications from major service providers (e.g., Facebook Messenger, Skype, Viber) where a meter is provided while using the application (i.e., no prediction of QoE). Only Meteor [START_REF]Meteor: Free internet speed & app performance test[END_REF] matches our goal of linking network measurements to multiple service QoE, while RTR-NetTest [START_REF]Rtr-nettest[END_REF] provides user-friendly meters per each network QoS metric. These tools are still either greedy at the network measurement level or approximate in terms of QoE estimation.

B. Network sensing

Measuring cellular networks from a crowdsourcing perspective is subject to several constraints. Since this approach relies on users' smartphones, we must take into account the limitations of the mobile OS, the battery consumption and not to over-use the mobile data plane while performing measurements. Generally, popular applications in this domain, e.g., [START_REF]Ookla. speedtest.net[END_REF], [START_REF]Meteor: Free internet speed & app performance test[END_REF], [START_REF]Sensorly -unbiased, real-world mobile coverage[END_REF], [START_REF]Rtr-nettest[END_REF], [START_REF] Huang | Mobiperf: Mobile network measurement system[END_REF], use a similar methodology to estimate the network conditions: ICMP ping for delay and loss measurements and TCP-based downloads/uploads for bandwidth measurements. While being very effective and precise, these techniques, especially the TCP based one, consume tens of megabytes per measurement session. Thus, these applications usually leave to the user the task of triggering manually each measurement and so, do not propose a periodic measurement plane. Among all the approaches we found in the state of the art, only Sensorly [START_REF]Sensorly -unbiased, real-world mobile coverage[END_REF] seems to be fully tailored for continuous mobile network sensing by allowing users to perform network measurements in the background. While being adapted to this use case, Sensorly uses TCP based speed tests to estimate the bandwidth and therefore consumes a considerable amount of data when not in passive mode.

While not being officially supported on mobile devices, iPerf [START_REF]iperf -the tcp, udp and sctp network bandwidth measurement tool[END_REF] can be used on smartphones via third party applications (e.g., [START_REF] Bhprdh | Magic iperf including iperf3[END_REF]). Being aimed for a technical audience, iPerf allows to perform several types of measurements. The possibility to run UDP-based measurements is of particular interest since UDP-based measurements are lighter than TCPbased ones. Still, the UDP-based measurements in iPerf cannot be as light as the ones we propose in ACQUA because of the additional signaling overhead (more details in Sec. IV).

As a conclusion, and as stated in Table I, we can see that existing mobile applications specialized in network monitoring do not generally offer QoE insights but rather technical ones (i.e., network QoS metrics). Moreover, none are suited to perform frequent measurements and track connection quality over time without heavily affecting the user's data budget.

III. PLATFORM

ACQUA is a user-centric platform whose main part is a mobile application (currently available for Android devices directly on Google Play Store [START_REF]Acqua (beta) -available on google play store[END_REF] or via the project website project.inria.fr/acqua/) that senses the network and provide an estimated QoE together with statistics on network access performance. The User Interface of ACQUA (see Fig. 1) is meant to be user-friendly; it is adapted to general users not familiar with the network domain, but also to expert ones. This is achieved by presenting the Quality of Experience of popular applications (dark blue tabs), as meters that summarize the network conditions. Other light blue tabs show statistics on network Quality of Service, i.e., the output of our measurement system (more details in Sec. III-A); these latter tabs target an audience familiar with network technology. We also provide a feedback form where the end user can submit her/his feedback following the same definition of QoE. We plan to use this feedback to test and improve our models by confronting them to real life scenarios.

A. Sensing the network

ACQUA measures the network periodically with a frequency that can go up to one session per minute. In each session, we collect both passive and active measurements. The active measurement module, developed as a standalone expendable Java module, consists of two measurement tasks:

• ICMP ping, that allows to estimate:

-RTT, Round Trip Time (identified in the UI as ping); -Download / Upload loss rate, percentage of packet losses; -Download / Upload jitter, variability of the delay.

• UDP burst, that allows to estimate:

-Download / Upload UDP throughput. Both tasks perform end-to-end measurements towards our edge servers. As said before, our long term objective is to deploy those servers (i.e., landmarks) close to the users so as to capture the access performance in a faithful way. For now, and during the proof of concept phase of ACQUA, we deploy them in our laboratory and make sure they are well dimensioned and well connected to the Internet. On the measurement overhead side, excluding IP, ICMP and UDP headers, the ping task consumes 400 bytes (i.e., 100 probes of 4 bytes each) and the burst task consumes 70 Kbytes (i.e., 5 separate traces of 10 probes of 1400 bytes each). While a single burst trace could be used, we send multiple ones per session to smooth the results of our measurements and reduce the bias effect. The overall data consumption per session is therefore no more than 71 Kbytes, far less than what TCP based tools consume.

At the end of the active measurements phase, passive measurements and other meta-data are collected using the Android API. We make sure not to compromise the privacy of end users by not collecting sensitive information as the IP address and the GPS position. Table II

B. From network performance to QoE

The advantage of using Quality of Experience as a KPI is twofold. First, it allows to summarize the network conditions as a single meter. Second, this meter is easy to understand by even non specialized audience. Instead of targeting a single application and its characteristics as input, we focus only on the network state as input (i.e., delay, throughput, jitter and loss rate). Our general framework can thus be reused for any number of applications (or application's specific features, e.g., different video streaming qualities). So far ACQUA has two QoE models that we briefly describe next. The preparation of other models for other applications is ongoing.

1) YouTube video streaming: ACQUA uses a model in the form of a decision tree, calibrated offline with controlled experiments, to link network level measurements (e.g., delay, throughput and loss rate) to a QoE level defined as a function of the application level metrics of overall stalling time and initial join time. Indeed, YouTube QoE is known to decrease fast with the join time and the duration of stalls. Explicit functions exist in the literature to capture this dependence, further details in [START_REF] Khokhar | On active sampling of controlled experiments for qoe modeling[END_REF]. We stream thousands of videos in variable network conditions, then for each streaming session, we measure the join time and the duration of stalls, which together give us an estimation of the QoE for this session on a scale from 1 to 5. Later, Supervised Machine Learning is used to build the decision tree that links the network conditions to the QoE level, thus yielding the model used in ACQUA.

2) Skype voice call: We collect data from a controlled environment where Skype calls are initiated through a prepared gateway. Network conditions are artificially changed and their impact on the quality of the call is noted based on the Skype QoE meter embedded in the Skype application (which models in somehow the average feedback of the Skype users for the same network conditions as estimated by Skype). We then use the resulting dataset to train a QoE model in the form of decision tree and we embed it in ACQUA [START_REF] Spetebroot | From network-level measurements to expected quality of experience: The skype use case[END_REF].

C. Power consumption and other features

Battery consumption of ACQUA has also been investigated. We ran ACQUA with default and greediest configuration (one measurement session per minute) on fully charged devices (different brands) for different time periods (e.g., 12 and 24 hours). Android feedback on battery consumption by ACQUA recorded less than 7% and 15% for 12 and 24 hours usage respectively. This consumption seems reasonable given the high frequency of measurements, and can be clearly reduced by increasing the inter measurement session period. We aim at lowering battery consumption in future releases by using new Android tools such as work manager and job schedulers.

In terms of other features of the project, ACQUA also presents time series and personalized statistics to the end user. These can be accessed through tabs and stats menu. Moreover, ACQUA has a public web interface with updated maps compiled by aggregating users' data. With no GPS info on ACQUA, we used a crowdsourced dataset 1 to get the approximate position of each network cell.

IV. ACQUA MEASUREMENTS VALIDATION

In this section, we focus on the validation of our UDP based bandwidth measurement method. In particular, we compare it 1 http://cellidfinder.com to the reference tool iPerf3 [START_REF]iperf -the tcp, udp and sctp network bandwidth measurement tool[END_REF]. Indeed, ACQUA uses UDP instead of TCP to estimate network available capacity. This choice is made to ensure continuous network sensing with low data consumption. In fact, TCP measurements require long traffic traces to derive accurate bandwidth measurements, otherwise with small traces (i.e., few Kilobytes as targeted in ACQUA), they tend to provide noisy estimates mainly because of the slow start phase, the closed loop of TCP, and the buffering at the receiver. To further study how TCP handles small sized traces, we performed a test with iPerf3 in TCP mode while fixing the size of the trace to 14KB (i.e., same as used in ACQUA for one train of UDP packets) and we artificially changed the network capacity. In Fig. 2, we can observe how TCP overestimates the network capacity when set to consume as ACQUA. We believe this happens when a consistent part of the stream gets buffered by the receiver and delivered as one large block to the application. Next, we compare our measurement method with a similar concept from iPerf3 based on UDP. While ACQUA being able to track the enforced bandwidth, Fig. 3 shows that iPerf3 tends to underestimate available bandwidth when using same burst size. This is mainly caused by the initialization delay in iPerf3 between the start of the session and the first probe sent, which biases the results of measurements based on small traces. Another interesting point to note is that iPerf3 is not designed to work with very small UDP traces too; it is not possible to reliably send the exact amount of data requested since the receiver notifies the sender when to interrupt the burst (i.e., when all the requested probes have been received). During this notification time probes will be sent by the sender and therefore will exceed the expected budget. Given those results, it is clear that, while not being technically novel in the field of network measurements in general, using UDP in ACQUA (i.e., small traces) is a solid and relevant way for continuous mobile measurements with low data consumption.

V. QOE TROUBLESHOOTING WITH ACQUA

In this section, we present first results using the ACQUA dataset (1 million records starting September 20172 ) for the sake of network and QoE troubleshooting. We use descriptive and inferential statistics to understand the occurrence of bad estimated QoE and point to local factors causing such phenomena. While only introducing first troubleshooting results, the ultimate goal is explained in Sec. VII. Further details on this initial study can be found in [START_REF] Belmoukadam | Data driven approach for network troubleshooting and qoe monitoring[END_REF].

For space constraints, we only show results related to the estimated QoE of YouTube streaming. Without losing generality, we transform the QoE multi-class problem (ranked 1 to 5) into a binary problem easy to handle:

A. Radio Access Technology

Fig. 4 illustrates the span of Bad and Good YouTube QoE over the different radio access technologies as recorded by ACQUA. In 2G, the proportion of bad QoE is more than 80% and decreases to 45% with 3G and even less around 5% with 4G and WiFi. ACQUA dataset reflects the fact that radio technology drastically impacts video streaming quality.

C. Mobility

The passive measurements collected by ACQUA include the serving cell id as well as the timestamp of the measurements. We use this information to build our mobility meter around the number of cell changes per time window and study the relationship between mobility and Bad QoE occurrence. We believe our approach is novel in studying mobility in the absence of GPS information. We consider a 10 min time window; a larger window would bias our estimation by diluting the effect of mobility, and a smaller window would not allow to catch the transition between cells. Fig. 6 shows average YouTube QoE versus mobility rate. Overall we observe that mobility rate is inversely proportional to average QoE. Still, our study shows that some technologies are more impacted than others by the mobility of end users.

D. Hint on congestion

We continue looking for other factors that can be passively collected inside the mobile and that point to network congestion from estimated QoE viewpoint. With ACQUA recording the time of measurements, and as time is an indication of network load, we study the relationship between QoE and different time metrics (e.g., Hour and Weekday). For example, and as pointed in Fig. 7, we were able to identify time intervals with bursts of Bad QoE during night and afternoon on each day, whereas Good QoE does not show the same behavior. Time of the day can thus help to interpret network problems. VI. LIMITATIONS 1) Lightweight measurements: While being effective, our bandwidth measurement method has some limitations. First, we noticed that high-speed bursts of a few packets cannot be handled correctly by Android due to buffering at the receiver's end. With our current trace size, we can reliably estimate a throughput of up to 20 Mbps. Over this value, our measurement plane starts to lose precision. We believe this limit still allows us to fulfill our goal of QoE estimation for most of services nowadays whose bandwidth requirements are usually lower than this limit. Another limitation come from the fact that UDP traffic might go blocked or rate limited by middleboxes deployed inside the network. To counter this, our future plans include the addition of TCP based measurements launched by the user on a manual basis.

2) QoE models: Due to the generality of our framework, our QoE models are less effective than tailored models targeting single applications. In fact, we do not require the access to application QoS metrics but only use network QoS metrics as inputs to our models. This slightly reduces the accuracy of our models, but most importantly permits us to perform a cross-application prediction of the QoE without the need to run the applications themselves in the mobiles.

VII. CONCLUSION AND FUTURE WORK

We presented ACQUA, a user-centric lightweight platform tailored to continuously sense mobile networks. ACQUA improves the transparency of networks by proposing simple meters to end users about their estimated QoE, while still providing a rich set of technical details. ACQUA also solicits users for their real QoE. Even with a small number of users (100+ downloads), we managed to collect over 1 million measurement records. We summarized the main ideas behind ACQUA and motivated its measurement strategy based on UDP. We also presented first results concerning a set of features that can be collected locally and passively in the mobile and that show correlation with bad network experience.

ACQUA currently embeds two QoE models related to YouTube streaming and Skype calls. The work will continue towards covering other applications of interest to the user. We also count on users' feedback to enhance our models based on standards, by confronting them to real life scenarios. We would like to assess how QoE in reality differs from what is predicted by standards and controlled research work.

Following the sneak peek in Sec. V, we plan to extend the set of features that can correlate with bad network experience (e.g., Android version, battery level, cell load). We will seek a multilayer classifier able to predict in the limit of possible the bad estimated QoE for different applications, so as to shed light on its root cause in today mobile networks.

As conclusion, we aim to fill the gap left by conventional crowd-sourcing apps, such as OpenSignal [START_REF]Opensignal[END_REF] and Sensorly [START_REF]Sensorly -unbiased, real-world mobile coverage[END_REF], that provide coverage maps of QoS metrics without QoE consideration. Our platform will allow us to study network patterns among users and tackle mobile network troubleshooting challenges from a different perspective. Such information can allow all stakeholders, including end users, operators, and regulators to have a greater visibility into the mobile network performance w.r.t the targeted Internet applications.

Fig. 1 .

 1 Fig. 1. Main interface of ACQUA (Android application) Meters estimating Quality of Experience are presented in bars, similarly to cellular signal strength indicator. The objective is to make them understandable by a large audience. QoE values are defined by the expert models we use and correspond to Mean Opinion Score (MOS) values ranging from 1 to 5 (plus "?" when undefined). One can interpret them as: 1) (worst) No connection, the application should not work; 2) Barely usable, unreliable behavior; 3) Major issues, e.g., long delays; 4) Minor issues, e.g., tolerable delays; 5) (best) Normal behavior, the application should work as expected.

Fig. 2 .

 2 Fig. 2. iPerf3 (TCP mode, limited to 14KB, single stream)

Fig. 3 .

 3 Fig. 3. ACQUA vs iPerf3 (UDP mode, limited to 14KB, single stream)

•

  Classes from 1 to 3 represent the Bad QoE class (0). • Classes 4 and 5 represent the Good QoE class (1).

Fig. 4 .

 4 Fig. 4. Bad/Good QoE span per radio access technology

Fig. 5 .

 5 Fig. 5. Bad/Good QoE signal strength (dBm) distribution for 3G

Fig. 6 .

 6 Fig. 6. Average QoE per Mobility rate

Fig. 7 .

 7 Fig. 7. Bad/Good QoE CDF over Day hours in January 2018

  summarizes data collected out of one measurement session:

	Active Measurements	Passive Measurements
	RTT	User ID
	Download loss rate	Signal strength
	Upload loss rate	Mobile Operator
	Download jitter	Radio technology
	Upload jitter	Network cell (CID,LAC)
	Download throughput	WIFI (SSID)
	Upload throughput	Roaming status
	TABLE II
	DATA COLLECTED OUT OF MEASUREMENT SESSION

One month dataset regularly updated made available at http://wwwsop.inria.fr/diana/acqua/datasets/Android/

This work is supported by the French National Research Agency under grant BottleNet no. ANR-15-CE25-0013, by Inria ADT ACQUA and Project Lab BetterNet. Regarding ethical issues, the work is validated by Inria review committee (COERLE) and by the French data protection authority (CNIL).