Mathieu Saujot

Matthieu De Lapparent

Élise Arnaud

Emmanuel Prados

Elise Arnaud

Making Land Use -Transport models operational tools

Keywords:

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Introduction

This article aims at improving the understanding of conditions under which Land Use and Transport Integrated models (LUTIs) would be accepted and used by planners and practitioners. We conducted a qualitative survey among French practitioners about their experience and expectations regarding LUTI modeling, in order to complete and support our reflection based on the literature and our own experience as modelers. The rationale behind the survey is: to make clear the requirements on LUTI models from an end-user perspective; to understand to which extent such models may complement other planning approaches from an empirical standpoint ; to help organizing fruitful top-down and bottom-up relations between LUTI developers and end-users.

A large literature is dedicated to these models. Contrary to classical and widely used traffic models, which consider the urban structure as an exogenous input to simulate the mobility system, LUTIs simulate both the land use system and the transport system, as well as their interactions. All LUTI models represent the evolution of different markets (land, housing, transport, labor) but differ in terms of modelling theories and methods, e.g. aggregated or agent based, based on market equilibrium or dynamic processes (Jin and Wegener, 2013). Some articles present their main features and propose a review and a classification (Simmonds et al., 1999;[START_REF] Wegener | Operational urban models: State of the art[END_REF][START_REF] Wegener | Overview of land use transport models[END_REF][START_REF] Batty | Urban modeling[END_REF]Iacono et al., 2008). Others present the existing models in line with different frameworks (Hunt et al., 2005;Silva and Wu, 2012), their application to territories [START_REF] Feldman | Propolis: Planning and Research of Policies for Land Use and Transport for Increasing Urban Sustainability[END_REF]Lautso et al., 2004;Gayda et al., 2005;Sustaincity, 2013) or their historical development (Simmonds and Feldman, 2007). Theoretical issues are also debated, Jin and [START_REF] Wegener | The future of mobility in cities: Challenges for urban modeling[END_REF] and [START_REF] Anas | A response to the guest editorial: economics as the science for urban modeling[END_REF] for example discuss equilibrium and the evolution towards more dynamic modeling.

Other articles compare applications of different models (Hunt et al., 2001). This literature has been useful to develop and improve modeling principles and illustrates what we can theoretically expect from the models to inform policymakers. However it says little about the frequency of their use, their maturity for operational use and their potential impacts for planning and decision making. The discussion is generally oriented towards technical and theoretical features, and the question whether the model can support an actual planning decision is poorly considered. In other words, the main question addressed is how these models are representing the different aspects of urban systems, including a discussion on the advantages and limits of each modeling solution, based on theoretical analysis. Hunt et al. (2005) for example aim at providing with a more practical evaluation of the current modeling framework as a guide to practitioners. Yet, they mainly focus on the formal characteristics of the modeling frameworks and the way they represent the different dimensions of urban systems. A concrete discussion on how these models were used and on the practical difficulties of modeling is missing and the suggested improvements are not taken from practice but from an ideal model.

Generally, applications are presented as evidence that the model is a useful tool, but they rarely take the planners' practical needs into account. As observed by Vonk et al. (2005) and [START_REF] Wegener | From macro to micro-how much micro is too much?[END_REF], the Planning Support System (PSS) and LUTI modeling community is often focused on academic issues, with a "strong emphasis on the supply side" (Vonk et al., 2005, p. 1) and a lesser concern to investigate the practical and operational use of models by practitioners.

Whether these models can be operational and under which conditions they can improve planning methodologies and policy design are important issues that must be discussed in detail. Originally the objectives of LUTIs were twofold [START_REF] Batty | Progress, success and failure in urban modeling[END_REF][START_REF] Batty | Urban modeling[END_REF]Klosterman, 2012;[START_REF] Lee | Retrospective on large-scale urban models[END_REF]:

1) improve, develop and test a theory for urban systems;

2) improve policy design and planning methodologies;

We still lack research on the latter. Even if some elements are available based on the experience of planners, researchers [START_REF] Lee | Requiem for Large-Scale Models[END_REF][START_REF] Lee | Retrospective on large-scale urban models[END_REF]Klosterman, 2012), or modelers [START_REF] Waddell | Integrated land use and transportation planning and modeling: addressing challenges in research and practice[END_REF]Timmermans and Arentze, 2011), this discussion is only occasionally the main purpose of the article and rarely based on a dedicated methodology. [START_REF] Lee | Requiem for Large-Scale Models[END_REF][START_REF] Lee | Retrospective on large-scale urban models[END_REF] was one of the first to discuss LUTIs from an operational planning view, pointing out the inherent difficulty of using complex modeling tools to feed a decision making process. Klosterman (1994aKlosterman ( , 1994b) ) and [START_REF] Wegener | Operational urban models: State of the art[END_REF] also highlighted this point. [START_REF] Wegener | From macro to micro-how much micro is too much?[END_REF] discussed the disaggregation trend in modeling and the technical difficulties of using microsimulation modeling in planning. [START_REF] Waddell | Integrated land use and transportation planning and modeling: addressing challenges in research and practice[END_REF] Noteworthy contributions also come from the PSS literature, even though they are not focused primarily on LUTIs. Vonk et al. (2005) investigate the bottlenecks blocking a wide usage of PSS (including LUTIs and other tools) with a survey of people involved in PSS (mainly academics and researchers). This paper incited us to use a similar approach specific to LUTIs, with more details on the use of the model and a focus on local authorities. [START_REF] Brömmelstroet | Developing land use and transport PSS: Meaningful information through a dialogue between modelers and planners[END_REF] shed light on obstacles that explain low levels of use. They also explore the planning process to understand which type of methods and tools would best fit as a planning support. They clearly show the necessity to connect end users (State transport services, local transport authorities, planning agencies, consultancies) and modelers through specific procedures and tools. Based on the "knowledge use" literature, Gudmunsson (2011) provides a framework for understanding the gap between transport modeling efforts and planning needs, and potential ways to diminish this gap. He underlines the need to carefully match policy expectations with the outputs the model can provide.

Other works worth noting are Still et al. (1999) and [START_REF] Ewing | Comparing Land Use Forecasting Methods[END_REF] who address the practical use of LUTIs with an assessment of the different methodologies used to inform planning.

As highlighted by Klosterman (2012, p. 1), modeling "reflects more fundamental assumptions about the limits of science, the role of the public, and the nature of planning", and requires thinking in terms of policy making and not only in terms of technical issues. Although LUTIs greatly improved during the last two decades, they are not yet widely disseminated. End users still seem indecisive about using them. As a result, they are primarily considered as research objects. The gap between lab application and operational use for planning practice is still to be filled [START_REF] Wegener | From macro to micro-how much micro is too much?[END_REF]Silva and Wu, 2012;[START_REF] Brömmelstroet | Developing land use and transport PSS: Meaningful information through a dialogue between modelers and planners[END_REF]Aashto, 2007;[START_REF] Nguyen-Luong | Les modèles transport-urbanisme : de la théorie à la pratique[END_REF]. Thus we still need to better understand how LUTIs are currently used, the barriers to their practical use and how they could better inform planning decisions and transport policies in practice. Doing so should help us identify the priorities for a LUTI agenda.

The rest of the paper is organized as follows. Section 2 defines our overall methodology. In the third section, we describe the current level of LUTI's practical use in France as well as in Europe and in the United States. In the fourth section, we specify the different purposes of using a LUTI and the type of the expected value added. The fifth section addresses the obstacles facing practical LUTI use and suggests some ways to overcome these difficulties. We draw our conclusions in the last section.

Overall methodology

A survey with 20 questions about urban modeling practice and demand for LUTI models was prepared. Hardy's survey (Hardy, 2011) 1 was used as a basis for several questions, and has been adapted to our own purpose. Our survey was sent to 30 French modeling practitioners2 (mainly in transport field) between summer and autumn 2013. We received answers from 15 of them. They constitute a representative panel of the types of relevant stakeholders (consultancies, State departments, local authorities and planning agencies). To improve the quality of the survey and facilitate the process, the sample was targeted in this way: 1) a good level of modeling and the possible presence of innovation; 2) a diversity of actors; 3) when possible, existing contacts with researchers of the CITIES project consortium 3 . Of course, this strategy 4 may introduce a bias, for example, toward an overestimation of the interest that LUTIs represent for the whole planning community, because we speak with transport modelers. Yet this does not necessarily mean that they are proponents of LUTI modelling. In any case, the results of the survey should be at least representative of the group of stakeholders who are real potential users in the medium term. The composition of the panel was: 5 transport authorities, 3 representatives of the State at the local level, 4 consultancies, 3 public planning organizations.

A survey description is given in the appendix. Analysis of the survey was twofold: qualitative analysis of the open-ended questions and percentages on the answers among respondents for the other questions 5 . Open-ended questions were used to evaluate the maturity of the organization in terms of modelling: dependence on consultancies, state of advancement on transport modelling. It helped us to confirm that the sample is composed of organizations with enough experience in terms of transport modelling to begin considering LUTI modelling. It also confirms our own experience i.e that specialized consultancies are crucial since public organizations do not have sufficient staff (both in Officials and the Association of Metropolitan Planning Organizations. The purpose was to explore the "role that a simpler transportation and land use modeling approach can play to support decision-making within metropolitan planning" (Hardy, 2011).

terms of competences and quantity).

In order to complete the results of the survey, we also conducted 8 qualitative interviews with key actors of transport at the French national and local level: a high level expert of the ministry of sustainable development, a modeler from French national railway network (RFF), the chief economist of the Société du Grand Paris (SGP), modelers from Île-de-France urban transport authority (STIF) and Greater Lyon area, and three consultancies. These interviews helped us to understand the maturity level of LUTI modeling and the conditions for LUTI diffusion.

By combining experience of the project consortium in implementing LUTI models, the survey and the interviews, we assume that we have a representative view of the French context. This study is completed by a review of the scientific literature that feeds our reflection and extends the work outside France.

Useful LUTIs but are they used?

United States and Europe

As stated earlier, a gap between research and practice exist in Europe and in the US6 . As stated in Iacono et al. (2008, p. 15): "Most of the newer generations of microsimulation models are designed with the objective of making them more policy sensitive. Unfortunately, few of them have yet reached a point where they can be fairly evaluated on this criterion". In a recent survey in the US, [START_REF] Lee | TMA/MPO Modeling Activity Survey, Fredericksburg Area Metropolitan Planning Organization[END_REF] shows that 47% of Metropolitan Planning Organisations (MPO) of more than 200 000 inhabitants are doing both transport and land use modeling. 46% of them use transport-only models.

47% use transport and land use models, but only 28% carries out an integrated planning (mostly using Urbansim, Pecas, Delta, Cube Land). The remaining 7% do not use any kind of modelling tool.

Other tools are based on graphical interfaces, ad hoc home applications, qualitative tools, etc. Hardy (2011, p. 122) observed that complex tools like LUTIs are not accessible to many planning agencies in the US, and that "The current literature is not clear as to what role computer modeling tools can have in the context of metropolitan planning in the U.S". [START_REF] Ewing | Comparing Land Use Forecasting Methods[END_REF] observed that methodologies are yet neither stabilized nor standardized in the US: in particular it is clear that the use of models is not automatic and that expert panels are still involved. Apart from the UK, which seems to be a place where LUTIs are the most commonly used for planning [START_REF] May | Developing a set of decision-support tools for sustainable urban transport in the UK[END_REF], LUTI modeling does not seem more developed in Europe. We do however highlight the recent Sustaincity project7 that contributed to the development of LUTI models and helped increase their popularity in Europe.

French context

Operational use of LUTIs for the simulation of regional planning policies is still an exception in France, despite important research investments and recent interest of planning agencies. LUTIs have only been developed for research projects since the mid 2000's: Urbansim [START_REF] Nicolas | Simuler les mobilités pour une agglomération durable[END_REF][START_REF] Nguyen-Luong | Projet SIMAURIF (SIMulation de l'interAction Urbanisation transports en Région Ile de France): Perfectionnement et valorisation[END_REF][START_REF] Bonnafous | MOSART et le projet PLAINSUDD : une plate-forme de modélisation et simulation de l'accessibilité pour l'aide à la décision et l'aménagement du territoire[END_REF]; TRANUS (Saujot, 2013); and Pirandello [START_REF] Delons | PIRANDELLO an integrated transport and landuse model for the Paris area[END_REF]. To date, no French local authority for planning or transport has appropriated the LUTI developed by academics specifically on its own territory, or developed its own LUTI.

In fact, the only example of an operational use of a LUTI is the Grand Paris project8 , undertaken by the "Société du Grand Paris" (SGP).The Grand Paris project is the most important urban project in France, with a €30 billion investment in the next 20 years, mainly for new transport infrastructures.

The SGP is in charge of the socio-economic impact assessment and they have constructed an ambitious program around LUTIs. A scientific committee has been set up to assess the modeling work done in parallel by three teams, each with a different tool (Relu-Tran, Urbansim, Pirandello).

After four years, work is in process, but preliminary results show that using LUTIs for an operational project is much more difficult and time-consuming than expected. Moreover the data collection task was under-estimated and the question of the relevant scale of zoning was a permanent debate. It is difficult for the experts of SGP to obtain satisfying answers about the results and behavior of the models, and reciprocally it is difficult for modelers to explain precisely the differences between the respective models' results for a given scenario. This example shows clearly that even with an important budget and a high level of expertise, using LUTIs to help decision making for a project like the Grand Paris is very difficult. Although this kind of project helps LUTI modeling gain in maturity, LUTI modeling is not mature enough for practical use at least in France. Nevertheless, despite the difficulties and the non-legal obligation to evaluate spatial effects, the SGP still considers that it is essential to continue to develop LUTIs for broader socio-economic evaluation in planning and sustains its efforts.

Another meaningful example can be drawn from the experience of the Greater Lyon 9 . A consortium of 5 local actors was created to set up a new common transport model (region, department, State at the regional level, Great Lyon, local transport authority), with an important budget. Formerly, each actor had its own transport model. Their decision to use modeling for local decision making via such a collective platform was innovative, all the more that modeling was not so popular few years ago in this area. They considered the possibility of using a LUTI instead of a classical 4-steps transport model but concluded that it was not the right choice for three reasons: a lack of appropriate guidelines for LUTI implementation; insufficient expertise both inside and outside the consortium to ensure a good choice of a model, and high risk relative to a long and uncertain process of building the model.

As far as the consulting firms specialized in transport modeling in France are concerned, no ready-touse commercial LUTI package is available at this point. The market situation (low visibility in terms of demand, financial constraints and short implementation periods) makes it difficult for consultancies to develop their own models. This pushes some firms to think that LUTIs are not well adapted to current public demand. For others, it reinforces the need to find a strict trade-off between quality and simplicity of the LUTIs. Beyond the model, the general diffusion of LUTIs diffusion would require the development of national guidelines. Such guidelines exist for conventional transport modeling but not for LUTIs, and the scientific literature is generally of little assistance for end users. Finally, not all consulting firms have the same level of trust in the robustness of LUTIs.

Different expectations about LUTIs when used for policy design

As mentioned previously, there are two potential benefits of urban modeling: one for science, to deepen our understanding of complex interactions but also to test and produce new theories, and the other for policy making and to design operational policies (e.g. Klosterman, 2012;Iacono et al, 2008;[START_REF] Batty | Progress, success and failure in urban modeling[END_REF][START_REF] Lee | Retrospective on large-scale urban models[END_REF], including a variety of prospective exercises [START_REF] Banister | Transport Futures: Thinking the unthinkable Transport Policy[END_REF]. These two fields are not uncoordinated as the development of new tools is often aiming at both objectives. But with the increasing complexity of models, the most advanced tools are yet far from simple operational use and policy design. Thus the real purpose of modeling is not always clear, even when a LUTI is used in a policy design project. For example, as observed by [START_REF] Lee | Retrospective on large-scale urban models[END_REF] there is often an ambiguity between tactical and strategic use. In broad outline, strategic use means use of a LUTI model to evaluate long-run planning options without looking for very detailed outcomes. 9 Second largest agglomeration in France.

Tactical use means use of a LUTI to detail and make short-run planning choices. Ambiguity comes from the fact that strategic and tactical planning are closely connected and that current development of LUTI models strive to do both at the same time.

To help policy design, we need to better understand how a LUTI can be used, i.e. which outputs can be provided, and in which step of the decision making.

Policy design: which outputs?

Our survey shows that, for French stakeholders, it is important to take into account transport and land use in an integrated way, and modeling tools are an interesting way to do so. Alongside other methods and types of analysis, modeling tools are considered important to evaluate or design land use and transport policies.

Another key lesson from our survey and interviews is that end users have very heterogenous  To evaluate effects of transport on land and housing prices (when data and models will be enough integrated to provide such outputs at a very detailed geographical scale);  To assess environmental, social and economic impacts of urban dynamics and urban development scenarios [START_REF] Nicolas | Simuler les mobilités pour une agglomération durable[END_REF]Saujot, 2013);  To test and recommend land use and transport policies to mitigate urban sprawl or improve sustainability (Gayda et al., 2005;Lautso et al., 2004);  To perform an overall cost-benefit analysis of a transport project, accounting for wider economic benefits;  To present projects and discuss them with stakeholders, LUTIs being used as a support for the debate and participatory process.

Such a large spectrum of objectives has the following implications. First, users' requirements for LUTI modeling are more extensive than for travel demand modeling and traffic simulation. It actually superimposes land use modeling and related planning problems to the latter. This clearly makes the development of a commercial offer more complex since the demand is not precisely defined and may strongly vary from one case to another. Second, when presenting and judging a LUTI and its application, one should always refer to the specific outcome(s) that are expected from the model and show consistency between the objectives of the application and the structure of the model. Third, it has implications for calibration and validation goals and procedures.

Policy design: which step of the decision making?

It is not enough to define the expected outputs. We also need to specify the step of planning targeted by the use of a LUTI, and the way its outputs could be used by end users in the decision making process. The fundamental goal is to adjust the models to their role in the policy making process, as underlined by Vickerman (2008). Indeed, both PSS literature (Gudmundsson, 2011;Brommelstroet and Bertolini, 2008;Klosterman 2001) and science-policy interface literature [START_REF] Sager | The Political Relevance of Planners' Analysis: The Case of a Parliamentary Standing Committee[END_REF]Vechionne, 2012) reveal the complexity of the link between modeling tools and decision-making process. As stated by Nilsson et al. (2008, p. 3), "more than three decades of policy analytic research remind us that the inter-relationship between assessment tools, the evidence they reveal, and their use by policy makers is unlikely to be straightforward".

As in Hardy ( 2011), in our survey, we proposed different steps of policy design where LUTIs could be used: 1) Exploration of planning possibilities and different strategies 2) Debate around scenarios and consultation with stakeholders 3) Strategic analysis of different scenarios and policies 4) Tactical assessment (design of projects). This 4 step representation is a strong simplification of the real planning process but remains useful to understand the role of LUTIs. Moreover each step requires a different prospective method, as described by [START_REF] Banister | Transport Futures: Thinking the unthinkable Transport Policy[END_REF].

As shown in Fig. 1 10 , respondents of the survey consider LUTIs as useful tools to explore possibilities and strategies (1. and 3.) for the next 20-30 years, at an aggregated level (urban area). Tactical assessment and support for debate appear to be less important. Moreover, the survey reveals that the use of a LUTI would be most helpful in the process of defining the strategic urban planning document at the level of an urban area, which is coherent with the precedent answers. This is illustrated in Fig. 2. Even among identical end users, we observe very heterogeneous results concerning the impact assessment of transport infrastructure (a document which refers to a tactical use of the model, including a cost benefit analysis). This reflects indecision between a tactical use and a strategic use, yet identified as more relevant in figure 1.

Another interesting aspect of LUTIs, prior to policy design and not often considered, should be mentioned. LUTI modeling can help team up transport and land use planning communities. It can help to connect the two visions before entering the steps of policy design. The simple fact that a LUTI asks for data from many different domains is a way to improve communication and common knowledge of a team in a planning agency. This is a lesson from our own implementations of LUTI models. For an actor like a transport authority, cooperating around a LUTI can also be a practical approach to better understand urban dynamics and to take into account the long-run effects.

Generally speaking, LUTIs are a good tool for improving integration between transport and land use policies, an issue constantly discussed and pursued by research and practitioners.

Now that we have clarified the different expectations about LUTIs, we must evaluate to what extent they are used in practice to inform planning strategies.

What are the major obstacles to LUTIs use? A need for more bottom-up approaches

What prevent LUTIs from being widely used by local authorities? Do current LUTIs really answer the questions and practical issues local planning agencies are faced with on a daily basis? And are current LUTIs suitable with respect to their constraints and limitations? In this section, we use the data we have acquired during the fieldwork in addition to the review of the literature presented here to propose a framework and to elaborate ideas and propositions.

The main obstacles associated with these issues are threefold. First, it is difficult to match rather generic models with very specific and varied end users questions. Secondly, it is costly and challenging to implement and use a LUTI (capacity obstacles). Finally, there is no guarantee that the results of a dedicated LUTI will have any impact on the policy design (decision making obstacles).

Matching with end user expectations

A key idea is that one can only ask a numerical model questions it was created for. Even with the progress in computer sciences (language, algorithm) and technology (computational power), the numerical problems derived from urban systems are very complex, and we are forced to simplify reality in order to be able to solve them. As discussed by Shiftan and Ben-Akiva (2011) in the context of activity-based travel demand modeling, the proposed approaches must be able to capture "key behavioural aspects and policy sensitivities while remaining practical with reasonable requirements of computational resources". Without precise questions, it is not possible to reduce the model in the right direction.

However, our experience shows that end users usually do not know how to precisely formulate the questions they want to answer with the model. Indeed, they are not LUTIs experts and planning questions are very vast. Nevertheless, modelers need these questions in order to develop a relevant model. It is difficult to solve this knot not only because it is costly for end users to become familiar with LUTIs and to understand the questions they can address, but also because it is costly for researchers or consultants to develop different model versions in an iterative co-building process.

The interviews reveal that it is very difficult for non-specialists of LUTIs to follow the progress of science and to judge the value of a model. Workshops organized at the national level in France are not sufficient to really understand to what extent these models are operational, and to identify the pros and cons of each model (black box effect, complexity of these tools and a research dynamic that "adds complexity to complexity", and the difficulty for researchers or developers to underline the weaknesses and limitations of their model). To overcome these difficulties, workshops should go further in practical details and reveal the actual level of maturity of LUTIs. Since it is crucial to adapt the LUTIs to current key issues that correspond to real and practical needs of potential end users in a very pragmatic way, they should also help gather end users expectations. The development of userfriendly human-machine interfaces between modelers and end users is also very important, e.g.

VIBAT project offers interactive simulation tools like TC-Sim 11 , dedicated to supporting a collaborative planning approach.

Capacity obstacles: local resource limitation vs. growing complexity

Capacity limitations are strong binding constraints because of the growing complexity of LUTIs. This gives rise to the simple/complex model debate. The scientific community tends towards more and more complexity (Timmermans and Arentze, 2011) and this is favored by an increasing computational power (Iacono et al., 2008), see Fig. 3. The academic logic searches innovation and sophistication of modeling tools, and the usability is probably not the main objective of researchers.

Moreover Wegener (2011, p. 4) observes with [START_REF] Lee | Requiem for Large-Scale Models[END_REF] that the "urban modeling community largely retreated to academia". This approach focusing on tool development can be identified as "top-down".

As illustrated in Fig. 3, adding complexity has its limits, and simpler and easier modeling is still necessary (Hardy, 2011;Klosterman, 2012;[START_REF] Brömmelstroet | Developing land use and transport PSS: Meaningful information through a dialogue between modelers and planners[END_REF][START_REF] May | Developing a set of decision-support tools for sustainable urban transport in the UK[END_REF]. A LUTI model should be simple, not to be confused with simplistic. Illustration based on (Klosterman, 2012;Hardy, 2011;[START_REF] Wagner | Urban land use, transport and environment models, experiences with an integrated microscopic approach[END_REF] As depicted in Fig. 4, model complexity is a combination of theoretical complexity of a tool and implementation design. Concerning implementation of a model, one element allowing to reduce (at least partly) the complexity is the development of specific tools and methodologies to facilitate the calibration and validation processes. In any case, it is essential to better understand the level of the required complexity and to adapt a model according to the final objectives. A model also needs to be adapted to the local capacity. Complexity should therefore be understood as the confrontation between modeling and local capacity.

In this context, it makes sense to oppose the "top-down" approach, focused on tool development (by itself), to the "bottom-up" approach which focuses on operational planning needs and limitations, and the way to fulfill them, as illustrated by Hardy (2011), Klosterman (2012), [START_REF] May | Developing a set of decision-support tools for sustainable urban transport in the UK[END_REF] and [START_REF] Brömmelstroet | Developing land use and transport PSS: Meaningful information through a dialogue between modelers and planners[END_REF], and where more attention is paid to simplifying models. This bottom-up approach is also illustrated in Vickerman ( 2008), which stresses the need for a better understanding between policy makers and modelers. Bottom-up approaches have so far been neglected even though being essential to ensure pragmatic and operational LUTIs, since they could

Decision making obstacles

Even assuming that capacity obstacles can be overcome and that a LUTI can be built to serve in a decision making process, there is no guarantee that its outcome will be of any use. In this section, we analyze the difficulty to understand the model outputs before using them for decision making.

Black box effect

The "black box" problem, based on literature and our own experience, implies the difficulty to Vickerman (2008) stated, there is a paradox between the search for more modeling complexity, in order to represent the complexity of urban systems, and the black box effect; a paradox from which it is difficult to escape. [START_REF] Waddell | Integrated land use and transportation planning and modeling: addressing challenges in research and practice[END_REF] observes a conflict between transparency (which needs simplicity) and validity (which needs complexity). This clearly brings us back to the simple/complex trade off.

Interestingly, another way to look at this question of black box effect is from the philosophy of the model. Common experience tends to show that micro simulation (like Urbansim) or activity-based models (like Albatross) are closer to intuition than more classical tools (like 4-steps or Tranus for example), which rely on a more important abstraction. They are easier to understand for nonmodelers (see Timmermans and Arentze, 2011, for an illustration). We can distinguish two dimensions in understanding of a model: the global understanding of the model rationale, its architecture and functioning, and the ability to identify causal links between inputs and outputs.

Some models can be easy to understand in the first dimension and more complex in the second one.

To have an impact on practical planning, a tool needs to be understood in both dimensions: because various persons with different profiles and responsibilities are involved (decision maker interested in the philosophy, technician interested in causal effects) and because these two dimensions play a role at different steps of the process (first try with the model, scenario analysis, etc.).

The survey reveals that the black box effect issue is very considerable: among respondents, 9 considered it very important and 1 important. Moreover several lessons can be learned from the

answers to the open-ended question on how to solve this problem:

 To clearly distinguish input data, calibration data, parameters and outputs.

 To identify and explain the key inputs for the model results.

 To share and validate every step of the development with all partners.

 To use these models only for very prospective and strategic analyses, mainly for pedagogy.

Similarly, we think that it would be very beneficial to develop better guidelines for the implementation of the models and to disseminate this knowledge through training and teaching. All this would clearly help circumvent the dilemma "complex -representative" versus "simpletransparent".

Furthermore, the fact that modeling tools are not used in the planning process is explained very differently by planners and modelers. Modelers would often consider that models are not complex enough to represent reality whereas planners may not see the interest of having details and precisions through a numerical tool and would underline the need for stakeholder involvement (Brommelstroet and Bertolini 2008). For example, Timmermans and Arentze (2011) consider that the key point is to help decision makers understand the mechanism of the urban system by capturing the complex interrelationships of the problem under investigation. To do so, they call for more complexity in order to avoid the risk of bad decisions. For [START_REF] Mostashari | Stakeholder-Assisted Modeling and Policy Design Process for Environmental Decision-Making[END_REF], a more complex model or a better mathematical validation will not help to include modeling results in decision making, but rather stakeholder involvement will.

It is not the purpose of this paper to conclude on the pros and cons of complexity in urban modeling.

What we can say from our analysis is that in a theoretical world, complex models are promising and interesting, but in our very constrained world, planning agency capacities are limited. Our study sheds light on the needs for more bottom-up methods, which requires co-construction with endusers. This asks for resources. A complex model will draw most of the resources for its implementation, limiting the capacity to set up these collaborative procedures. Trade-offs must be made. For a broader diffusion of LUTIs in planning processes, academics should emphasize more bottom-up approaches.

Interviews reveal that a key issue for many actors (notably in France, for the Ministry of Ecology, Sustainable Development and Energy) is whether it is possible to provide rational and science-based tools to support specifically decision-making. This is especially the case through cost-benefits analysis, in a context where this method has progressively lost its importance in the decision making process. From this perspective, the question for LUTI models is the following: do LUTIs help decision making in a wider socio-economic evaluation, by providing clear, understandable and convincing results useful in a cost-benefit analysis? Can they provide sufficient value added that would compensate for additional complexity and black box effects they may generate?

How reliable are LUTI models? Obstacles in terms of calibration and validation 12

The low level of use and impact on decision making of LUTIs can also come from a lack of confidence in their results, partly related to the absence of a clear and consensual definition of calibration and validation in the literature [START_REF] Bonnel | A survey on the calibration and validation of integrated land use and transportation models[END_REF]. We define calibration and validation as follows [START_REF] Bonnel | A survey on the calibration and validation of integrated land use and transportation models[END_REF]:

 Calibration refers to the determination of parameters values  Estimation refers to the use of specific statistical or econometric methods and calibration is a process overarching the estimation process.

 Validation refers to testing the predictive power of the method using out-of-sample data.

 A calibration ensures that the model is acceptable; a validation is always partial since a test can only invalidate a model. LUTIs, as any numerical models, are inherently uncertain as they rely on both theoretical assumptions and data quality. There is no physical law ensuring reliability of models. Consequently, there are neither neutral models (Klosterman, 2012) nor objective rules to state that one specification is better than another (Sterman, 2000;[START_REF] Pfaffenbichler | The integrated dynamic land use and transport model MARS[END_REF]. Thus there is no absolute calibration or validation, both for simple and complex models. Moreover, new models cannot "meet all basic scientific criteria" and be used for theory testing, but they can provide "robust but contingent knowledge" (Batty, 2009, p. 6). Calibration and validation should be seen in this respect.

Calibration and validation procedures lead to theoretical, methodological, and practical difficulties (time, resource, data). The practical difficulties are one of the main obstacles of LUTI use. Moreover these procedures need to be understandable by end users in order to produce confidence in the model. To ensure the relevance of the model in line with its practical objectives, the criteria used to calibrate and validate the model must be based on relevant indicators defined by end users. Model testing should be specific to the type of expected use, see figure 5.

Wegener ( 2013) also observes that models should be adapted to new contexts for planning (climate change and increasing energy prices). Models should less be based on extrapolation of past trends and observed behaviors, and therefore should less rely on statistical calibration. A plausibility analysis would be more meaningful. This is directly related to the need to conduct a sensitivity analysis on top of a historical validation. Franklin et al., 2002;[START_REF] Duthie | Highway Improvement Project Rankings due to Uncertain Model Inputs: Application of Traditional Transportation and Land Use Models[END_REF]. It is likely that sensitivity tests using scenarios increase both the scientific value of the modeling process and the understanding of the modeling process by the end user/ practitioner. In our survey, answers show that sensitivity tests appear to be decisive to validate the model and increase transparency. We think that sensitivity analysis has several benefits:



Conclusion.

In this article, we propose another perspective for LUTI modeling that is based on user expectation rather than model specifications. In our analysis of the French case and the literature, we have identified the obstacles to a wide use of LUTIs. We have also shown the conditions for fostering LUTIs' practical use in planning. The survey and interviews have been a way to better define the possible use of LUTIs. Its seems that an opportunity exists for LUTIs, but also several obstacles: transport models are already considered as heavy and complex to use, whereas LUTI imply even more complexity and validation needs while evoking a black box effect.

Considering not primarily theoretical development and features, but the actual use of LUTI models, it appears that end users have a critical role to play. They have to guide researchers to adapt their tools to the needs and the constraints/limitations of the end users. The key idea is to reintegrate LUTI modeling as a key component into the planning process. LUTIs are at the interface of qualitative and quantitative standpoints. Since the context is very constrained in terms of resources and LUTI models are costly, and because the impact of modeling on the decision making process is not straightforward, it is mandatory to be very precise about the needs of end users in order to specify LUTI modeling requirements. Top-down approaches have been useful to provide different structures of LUTIs, but today we need more bottom-up approaches to make LUTIs more popular.

In order to overcome these difficulties and to move from top-down approaches to more bottom-up approaches, we have identified the following priorities:

 To focus on the practical conditions of LUTI implementation and to identify the needs of end users.

 To set up specific interaction procedures between end users and modelers, using visualization tools which favor the integration of LUTIs into the planning process (this could be done via a better association of PSS approaches and LUTI modeling).  To clarify and develop:

  described the many challenges -technical and nontechnical -of transferring modeling tools from academic research to planning agencies, and explained what has been undertaken for Urbansim development to increase the appropriation by end users. Timmermans and Arentze (2011), inspired by their own experience of modeling, analyzed the links between research and urban planning. Klosterman (2012) discussed the nature of modeling, using the simple/complex model opposition.

  expectations about LUTIs. Several objectives of LUTI modeling can be found among the different projects and actors:  To make the spatial distribution of jobs and population/households endogenous (potential use by transport authority);  To simulate effects of transport on land use (Nguyen-Luong, 2008);

Fig. 1 :

 1 Fig. 1: Survey results for which type of use a LUTI could be helpful.

Fig. 2 :

 2 Fig. 2: Survey results for the type of document a LUTI could help to define;

Fig. 3 :

 3 Fig.3: A move towards more complexity -drivers and limits of complexity;

Fig. 4 :

 4 Fig. 4: A representation of complexity; illustration based on Klosterman (2012), Hardy (2011)

  LUTI models among practitioners who have no direct control over the implementation of these models, because they do not trust what they can't understand. Moreover, as revealed by the interviews, there is sometimes not only the difficulty of understanding the way a model works but more fundamentally understanding what a model is. As Timmermans (2003) and



  To improve and validate the model. As a way to validate the model it improves the quality of modeling. There can be back and forth work between the calibration stage and the sensitivity tests.  To increase involvement of stakeholders. Via the sensitivity analysis, the end users can easily participate by proposing tests and scenarios and expressing expectations about the test outputs. By taking part in the design and validation phase of the model one can guarantee better ownership of the model by the stakeholders. To overcome the black box effect. It increases the ability of the end users to understand the processes. Tests should be chosen to help end users understand the most complex links and interactions in the model. This improved understanding naturally increases the confidence of end users in the model.

o

  calibration tools, o validation methodologies, o and a framework for sensitivity tests ... and to involve end users in all these stages.  To develop better implementation guidelines for LUTI models.  To disseminate knowledge through training and teaching.  Saujot, M. (2013), Economic analysis and prospective modeling to plan low carbon cities. The case of Grenoble using TRANUS+ model. Available from : Pastel database http://pastel.archivesouvertes.fr/pastel-00982385  Shiftan, Y., Ben-Akiva, M. (2011), 'A practical policy-sensitive, activity-based, travel-demand model', Annals of Regional Science, 47(3) 517-541  Silva, E., Wu, N. (2012) 'Surveying Models in Urban Land Studies', Journal of Planning Literature, 27(2) 139-152  Simmonds D., Echenique M. et al. Bates J. (1999) Review of Land-Use/ Transport Interaction Models. DETR, 72p. http://www.davidsimmonds.com/files/Review-of-Land-Use-Transport-Interaction-Models.pdf  Simmonds, D.C., Feldman, O. (2007) 'Advances in integrated urban/regional land-use/transport modeling using the DELTA package', Paper presented to the World Conference on Transport Research 2007, June 24-28, University of California, Berkeley.  Sterman, J.D. (2000) Business Dynamics -Systems Thinking and modeling of a complex world. McGraw-Hill Higher Education  Still, B.G., May, A.D., Bristow, A.L. (1999) 'The assessment of transport impacts on land use: practical uses in strategic planning', Transport Policy 6 (2) 83-98  Sustaincity (2013) 'Microsimulation, land use and transportation models for more sustainable cities in Europe: WP2 -State of the art', European Policy Brief, European Research Area.  Timmermans, H. (2003) 'The Saga of Integrated Land Use-Transport Modeling: How Many More Dreams Before We Wake Up?', Conference keynote speaker, 10th International Conference on Travel Behaviour Research Lucerne, 10-15. August 2003  Timmermans, H., Arentze, T.A. (2011) 'Transport Models and Urban Planning Practice: Experiences with Albatross', Transport Reviews: A Transnational Transdisciplinary Journal, 31(2) 199-207  Vecchione, E. (2012) 'Deliberating beyond evidence: lessons from Integrated Assessment Modeling', Paper prepared for the ISEE Conference, Rio de Janeiro, Brazil, 16-19 June 2012.  Vickerman, R. (2008) 'Models in policy and policy in models: integrating decision making', Chapter 12 in M Ben-Akiva, H Meersman and E Van de Voorde (Eds.): Recent Developments in Transport Modelling: Lessons for the Freight Sector. Emerald, Bingley, 2008  Vonk, G., Geertman, S., Schot, P. (2005) 'Bottlenecks blocking widespread usage of planning support Systems', Environment and Planning A, 37(5) 909-924

  Calibration and validation requirements depending on the type of use (elaborated by authors) Historical validation is generally considered a key step for validation in the research community. To be able to simulate a past development is a clue that the model is working correctly, but it is not a guarantee for prospective modeling.Waddell (2011, p. 7) speaks about "behavioral validity" to include more "intuitive understandings" of the model. Compared to "hard science" modeling, socioeconomic models face difficulties in collecting (sufficient) data and replicating results. In a context of low resources, developing a sensitivity analysis is particularly interesting[START_REF]  Wenban-Smith | Using Transport Models in Spatial Planning: Issues from a review of the London Land Use/Transport Interaction Model[END_REF]

		studies		measures
	Main	Easy and flexible to	Understandable by	Representation of key	Comprehensive
	requirement	use in prospective	stakeholders	mechanisms	representation
	for the model	studies
	Requirement	Aggregated	Test to ensure trust	More precise tests to	Tests at smaller scale
	for calibration/	sensitivity tests to	of the different	ensure that the	to verify the precision
	validation	show the reliability	stakeholders	selected indicators are	of the model
		of prospective		reliable
		studies
	Fig. 5:
		Exploration	Debate	Strategic analysis	Tactical assessment
	Value added of	Interactive support	Neutral/ unbiased	Support for policy	A lot of indicators with
	LUTI modeling	for prospective	support	analysis and ranking of	precise results

Hardy developed it for US practitioners from the American Association of State Highway and Transportation

The

French modelling practitioners were chosen all over France. We chose the most advanced organizations in terms of modelling, i.e those with the biggest likelihood to have an understanding and opinion about LUTI modelling, which is useful for our research.3 The CITIES project's ambition is to foster the use of LUTI, by developing methodologies and tools to facilitate their use (mainly numerical methods to help calibration and validation). The consortium includes mathematicians and computer scientists, most of French LUTI modeling teams and end users (planning agencies). Different LUTI are used (Urbansim, TRANUS, Pirandello). The project is financed by the French research agency (ANR).4 Via the choice of the interviewees (of whom a large part have connections with the researchers) but also because of the survey principle itself (where people who feel more concerned by the object of interest are more willing to answer).5 The percentage is computed by taking into account all the answers.

This choice was made because these areas correspond to most of the literature for this question, as well as to our own experience. Future works could fruitfully investigate other continents and especially South America where many LUTIs were developed.

FP7 project funded by European Union: http://www.sustaincity.org

http://www.societedugrandparis.fr/english

We are cognizant that this small sample is not enough to produce reliable statistics, and should rather be seen as a way to draw some trends, useful to complete and support our reflection.

It is worth noting that this section does not rely on the survey and interviews used in the rest of the article, but rather on our own experience, debates within our research consortium and the literature.

Acknowledgements:

The authors wish to thank the ANR research program and the EcosNord program for funding this research. The authors also wish to thank Ifsttar, Inria and Iddri for supporting this research, and the

 Franklin, J.P., Waddel, P., Britting, J. (2002) 'Sensitivity analysis approach for an integrated land development & travel demand modeling system'. Paper for presentation at the ACSP 44th Annual Conference Baltimore, MD, November 21-24, 2002 Gayda, S., Haag, G., Besussi, E., Lautso, K., Noël, C., Martino, A., Moilanen, P., Dormois, R. (2005) 'Sprawling Cities And TransporT: from Evaluation to Recommendations: SCATTER'. Project

Appendices

Foreword We first explain the goal of our project, then we briefly describe LUTI models and give a short example of an application.

Guidelines We assume that modeling is linked to political demand and we ask the respondent to answer considering the most relevant practice in their opinion and not considering the political demand.

Q1

Q6

Indicate how important it is to consider land use policies and transport policies in an integrated manner: 1) not important 2) moderately important 3) important 4) very important

Q7

Besides other types of analysis, indicate how important modeling tools are to evaluate such policies and to help define them: (same as precedent).

Q8

For prospective studies, at what scale should we mainly take into account these policies? 1) District 2)

Corridor 3) City 4) Urban area (city + suburbs) 5) Region

Q9

With what time horizon? 10 years; 20-30 years; 50 years Q10 LUTIs are essential: 1) to explore the planning possibilities 2) to debate scenarios 3) for strategic analysis 4) for tactical assessment

Q11 At what scale results need to be produced for each precedent task? 1) Plot 2) District 3) City 4) a merging of cities Q12 For which document would LUTIs be the most useful? 1) Impact assessment of large transport infrastructures 2) zoning at the city level 3) transport planning at the urban area level 4) urban planning at the urban area level Q13 Do you agree with the following assertions (no, rather, yes): 1) a LUTI is useful to forecast future urban development (long term) 2) a LUTI is useful to understand the complexity of urban systems 3) a LUTI is useful to compare trends and scenarios but not for forecasting 4) transport modeling is already sufficient, there is no need for more complexity with another modeling tool.

Q14 How to decide between a LUTI and your transport modeling tool? 1) Complementary use: tactical/ strategic use 2) Replacement by the LUTI 3) Interoperability: outputs of the LUTI used for transport modeling Q15 Numerical models are often considered as black boxes because the end user has a limited understanding of the calculus made by the model. To what extent is transparency important for you?


Georgia Cablea
email: georgia.cablea@gipsa-lab.grenoble-inp.fr

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

Christophe Bérenguer
email: christophe.berenguer@gipsa-lab.grenoble-inp.fr

Bearing faults monitoring in electrical rotating machines through three-phase electrical signals analysis

Condition monitoring methods based on electrical signals analysis have been used for mechanical and electrical fault detection for a while now. Moreover, the research focus has shifted from single-phase signals analysis to three-phase signals approaches. The main advantages of using three-phase approaches can be stated as separation of balanced and unbalanced electrical quantities as well as better performances in terms of mechanical faults detection. However, such approaches still have a low industrial penetration in part due to their relatively higher complexity compared to single-phase approaches. The current paper proposes an easy to implement method for condition monitoring of bearings, which takes into account the whole three-phase electrical signals. After presenting the theoretical development of the method, the algorithm for computing mechanical faults indicators is given. Moreover, the paper presents experimental results of the proposed approach, using electrical signals acquired on a dedicated test bench.

Introduction

Three-phase rotating machines are used in a wide range of configurations of electromechanical systems for different industries. Thus, they are affected by both mechanical and electrical faults. In this paper the focus is set on the signature of mechanical faults in the three-phase electrical signals.

Models for mechanical faults signature in electrical signals have been developed throughout the years. A model for bearing fault signature was proposed in [START_REF] Schoen | Motor bearing damage detection using stator current monitoring[END_REF] based on the fact that such faults trigger air-gap eccentricities related to the faults. Such models have been later improved [START_REF] Blodt | Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring[END_REF] and it was shown that bearing faults may induce torque variations as well as eccentricity. Thus, they can trigger phase and/or amplitude modulations in the electrical signals at well defined frequencies for each case. Later the models were extended for any type of mechanical faults [START_REF] Blodt | Distinguishing load torque oscillations and eccentricity faults in induction motors using stator current wigner distributions[END_REF][START_REF] Blodt | Mechanical fault detection in induction motor drives through stator current monitoring -theory and application examples[END_REF] , not just the bearings.

Methods to detect mechanical faults [START_REF] Blodt | Mechanical fault detection in induction motor drives through stator current monitoring -theory and application examples[END_REF] using electrical signals have initially focused on single phase stator currents and they are generically known as motor current signature analysis (MCSA) [START_REF]Electric machines: modeling, condition monitoring, and fault diagnosis[END_REF] . More recently the focus has shifted towards three-phase approaches. Most often these methods rely on the use of a three-phase transform, like the Fortescue transform [START_REF] Fortescue | Method of symmetrical co-ordinates applied to the solution of polyphase networks[END_REF] or the Clarke/Concordia transform [START_REF] Clarke | Circuit Analysis of A-C Power Systems[END_REF] , to combine the information in the three-phases and obtain the symmetrical components. Afterwards the obtained signals are demodulated [START_REF] Silva | Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach[END_REF][START_REF] Trajin | Hilbert versus Concordia transform for three-phase machine stator current time-frequency monitoring[END_REF][START_REF] Amirat | Wind turbines condition monitoring and fault diagnosis using generator current amplitude demodulation[END_REF][START_REF] Choqueuse | Diagnosis of threephase electrical machines using multidimensional demodulation techniques[END_REF][START_REF] El Bouchikhi | Stator current demodulation for induction machine rotor faults diagnosis[END_REF] in order to study the presence of fault-related modulations. However, these demodulation techniques are based on the assumption that the obtained symmetrical components can be approximated to be an analytic signals which is not always the case.

Previous works [START_REF] Cablea | Method for computing efficient electrical indicators for offshore wind turbine monitoring[END_REF] [START_REF] Cablea | Online condition monitoring of wind turbines through three-phase electrical signature analysis[END_REF] have proposed a method for condition monitoring of both mechanical and electrical faults that is based on correctly demodulating the symmetrical components corresponding to the electrical signals. The proposed method for mechanical fault detection is limited to modulations of frequencies lower than the fundamental frequency of the electrical signals. The current paper extends the approach to high-frequency modulations. The next section presents the proposed approach for mechanical faults detection. The section starts with the description of mechanical faults signatures in three-phase signals and then gives the proposed algorithm for obtaining mechanical fault indicators able to detect such faults. The third section of this article validates the proposed method using experimental signals acquired on a dedicated test bench. The last section of the article gives the conclusions and perspectives of the presented work.

Three-phase electrical signals analysis method for mechanical fault monitoring

In this section the content of electrical quantities in the presence of the signature of mechanical faults is presented. The expected behaviour of this signatures in the three-phase electrical signals is described and in the end mechanical fault indicators are proposed. Throughout the section the discussion is based on the three-phase currents. However, the same analysis can be preformed on the voltages.

Mechanical fault signature in instantaneous symmetrical components

The instantaneous symmetrical components (ISCs) are obtained from the phase currents by applying the inverse Fortescue transform:

  i + (t) i -(t) i 0 (t)   = 1 3   1 a a 2 1 a 2 a 1 1 1   F -1   i 1 (t) i 2 (t) i 3 (t)   (1)
where a = e j 2π 3 , F -1 denotes the inverse of the Fortescue matrix [START_REF] Fortescue | Method of symmetrical co-ordinates applied to the solution of polyphase networks[END_REF] , and the ISCs of the original three-phase signal are: the positive-sequence ISC (i + (t)), the negative-sequence ISC (i -(t)) and the zero-sequence ISC (i 0 (t)). In the rest of this paper, the transform defined in Equation ( 1) is referred to as ISC transform.

As long as the effects of the mechanical faults are present (torque variations and/or eccentricity) the fault signatures would be visible in the electrical quantities as modulations [START_REF] Blodt | Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring[END_REF] . When comparing the value of the modulating frequency to the carrier frequency we can either have low-frequency modulations or high-frequency modulations. Figure 1 shows the expected signatures for both low-frequency modulations and high-frequency modu-lations in ISCs for real-valued signals for which only the balanced components of the signals are modulated. There are three considerations that need to be made regarding this figure :   1. Firstly, there is a spectral redundancy in the ISCs. However, a full spectral analysis can be performed by only considering the modulations around + f 0 and this would be the case throughout the paper.

2. Secondly, if the negative-sequence ISC would also contain modulations, their amplitude would be really small. The amplitude of the modulations depends on the amplitude of the fundamental and for a functioning three-phase system, the amplitude of the negative-sequence ISC is very small. Thus, for mechanical faults detection throughout this paper, only the positive-sequence ISC would be considered.

3. Thirdly, the modulating frequency is assumed to be known. Indeed, this frequency can be easily determined using the kinematics of the system and the known operating conditions, like the rotating speed of the shaft. In the current paper, this frequency would be considered as an input of the algorithm. Previous works [START_REF] Cablea | Method for computing efficient electrical indicators for offshore wind turbine monitoring[END_REF][START_REF] Cablea | Online condition monitoring of wind turbines through three-phase electrical signature analysis[END_REF] have dealt with the case of low-frequency signatures of mechanical faults. An algorithm for extracting mechanical faults indicators by demodulating the electrical quantities has been proposed. The next section proposes an algorithm for the high-frequency case presented in Figure 1b. In this case, the modulating frequency is not negligible compared to the fundamental frequency. The algorithms will take into account the three-phase signal model as well as the previous observations. Thus, only modulations around the positive fundamental frequency + f 0 of the positive-sequence ISC will be analysed.

Algorithm for mechanical faults detection

The case of mechanical faults which have high-frequency signatures needs to be considered separately from the low-frequency one. Figure 1 depicts the two expected behaviours for these two cases. If the modulating frequency is higher than the fundamental frequency of the electrical quantities, as in Figure 1b, different aspects have to be taken into account:

1. The Hilbert demodulation technique previously used cannot be implemented as the necessary conditions are no longer fulfilled. In other words, the positive-sequence ISC cannot be assumed to be an analytic signal. For demodulated quantities of the positive-sequence ISC there was a single frequency band B centred around the fault characteristic frequency f m considered for the fault indicator computation. For non-demodulated signals two frequency bands are to be considered B l (the left-hand side band) and B r (the right-hand side band). These bands will be centred around f 0 ± f m . Figure 2 graphically indicates the considered bands.

2. As the literature explains [START_REF] Randall | Rolling element bearing diagnostics-A tutorial[END_REF] and the existing experimental data within the KAStrion project confirms, bearing faults characteristic frequency may vary by ±2%. While for low-frequency faults this variation may be negligible, for high-frequency faults this aspect needs to be taken into account when computing the fault indicator. In case of really high frequencies, for example 200 Hz, this leads to a rather wide frequency band that needs to be considered, i.e. the corresponding band [196 204] Hz is 8 Hz wide.

f I + (f ) -f 0 I * -(f 0 ) f 0 I + (f 0 ) Br f m Bl f m

Figure 2: High-frequency modulation bands

The ±2% variation aspect is specific to bearing faults and the calculation of fault characteristic frequency bands for each possible mechanical fault is beyond the scope of this paper. However, what is important to consider is that the proposed algorithm for mechanical fault indicator computation must be robust with respect to the width of the fault characteristic frequency band.

Figure 3 gives the general structure of the algorithm for computing mechanical fault indicators for faults which have a high-frequency signature. This structure is very similar to the one proposed for the low-frequency mechanical faults indicators. This simplified version of the algorithm structure has removed the demodulation step and the PSD is estimated directly for the positive-sequence ISC. Afterwards, the fault indicator is computed considering the two corresponding frequency bands. The next sub-sections will give more details into the proposed indicators. Indeed, several indicators are to be computed, not just one. The following indicators are not assumed to be optimal. However they are easy to implement and remain efficient in detecting the eventual faults.

1 3   1 a a 2 1 a 2 a 1 1 1   PSD estimation process S(f ) i(t) i + (t) m fault indicator computation

Fault indicators based on the signal energy

Fault indicator 1 A mechanical fault indicator has been proposed in [START_REF] Cablea | Online condition monitoring of wind turbines through three-phase electrical signature analysis[END_REF] for mechanical faults inducing low-frequency modulations. The corresponding indicator is to be computed over a narrow frequency band using demodulated signals and is expressed as:

m = (B) S( f ) d f (B) S h ( f ) d f , (2)
where B denotes the considered frequency band, S h ( f ) denotes a reference PSD obtained for a healthy system and S( f ) is the current PSD.

This first indicator proposed for the high-frequency modulations is an extension of the indicator for low frequency given in Equation ( 2). Instead of considering just the frequency band B, both frequency bands (B l and B r ) modulating the fundamental frequency f 0 are considered. The expression of this new indicator is given in (3).

m 1 = (B l ) S( f ) d f + (B r ) S( f ) d f (B l ) S h ( f ) d f + (B r ) S h ( f ) d f , (3)
where B l and B r denote the left and right frequency bands, S h ( f ) denotes a reference PSD obtained for a healthy system and S( f ) is the current PSD obtained for the system in an unknown state. In the case of an healthy condition, such an indicator stays obviously close to one, and tends to increase if faulty components appear in the signals.

In case of wide frequency bands (B l and B r ), the contribution of two new peaks (one on each side) might be hard to detect in an incipient stage. Indeed, this indicator might not be sensitive enough to new peaks regardless of the frequency bands width.

Fault indicator 2

The second indicator proposed is an improvement of the first. Instead of using the whole energy in the given frequency bands B l and B r , the indicator can be computed using the amount of energy higher than the noise floor.

The first step in computing this indicator is to estimate the noise floor in each of the bands.

The noise level can be estimated using a median filter over S( f ) for f ∈ (B l ∪ B r ). The obtained estimations are denoted S n ( f ) and they only exist over B l and B r . Afterwards, a difference between the PSD S( f ) and the noise floor is computed. The quantity is denoted S d ( f ) and is obtained as:

S d ( f ) = S( f ) -S n ( f ), for f ∈ (B l ∪ B r ) (4)
If the quantity S d ( f ) is greater than 0 then the current spectrum contains energy that is higher than the noise level. Thus, the proposed indicator only considers this amount of energy and is expressed as:

m 2 = (B l ∪B r ) S d ( f ) S d ( f )>0 d f (B l ∪B r ) S d h ( f ) S d h ( f )>0 d f , (5)
This indicator is normalised by the amount of energy computed for the healthy case.

Fault indicator based on frequency values

Fault indicator 3 The third proposed indicator uses the fact that there are two frequency bands that are symmetrical with respect to the fundamental frequency. In this case, a frequency will be obtained for each of the bands corresponding to the maximum difference between S( f ) and S h ( f ) in the given band. Thus, f l corresponds to the frequency for which (S( f ) -S h ( f ))| (B l ) has the maximum value and consequently f r denotes the frequency for which (S( f ) -S h ( f ))| (B r ) reaches maximum. The electrical fundamental frequency denoted f 0 corresponds to the maximum in S( f ). Considering that mechanical faults induce modulations, the interest is to determine whether the differences correspond to modulations. Thus, two new quantities are computed:

f m l = | f l -f 0 | f m r = | f r -f 0 | (6)
Based on the two frequencies indicating the distance between the fundamental frequency and the frequencies of the maximal differences, a fault indicator is computed as:

m 3 = | f m l -f m r | (7)
In case the two maxima differences correspond to a modulation of the fundamental frequency, the indicator m 3 would be null. A threshold can be set as the spectral resolution in order to ensure no detection is missed due to precision. In case the indicator is null, f m l and f m r indicate the modulating frequency itself.

This indicator is not very robust with respect to the content of the characteristic frequency bands. Indeed, it relies on the assumption that the modulation due to a mechanical fault represents the highest change with respect to the estimated healthy condition. This assumption is not necessarily true in all practical cases. For example one of the bands might present its highest change at a frequency location corresponding to a different modulation of a harmonic. The wider the bands the higher the chance is for them to also contain other components besides modulations of the fundamental frequency.

3 Experimental results

Experimental set-up

These indicators were tested using experimental data coming from a test-bench purposely designed and developed in the CETIM laboratory (Senlis, France) [START_REF] Bédouin | Endurance testing on a wind turbine test bench. A focus on slow rotating bearing monitoring[END_REF] . The bench emulates the structure and behaviour of a wind turbine, with an electrical motor replacing the wind turbine rotor, followed by a low speed shaft with the main bearing, a gearbox, high speed shaft and three-phase electrical generator. The operating conditions are determined by the speed of the low speed shaft. For the experiment considered in this paper a radial load force was applied on the output bearing (located on the high-speed shaft) leading to an accelerated deterioration of this bearing.

This experiment has run for ≈ 900 hours. At the end, the output bearing was dismounted and it was visually inspected. The visual inspection showed there was no flaking, but a distributed wear over half the outer race (remained smooth but cracked). There were also traces on the bore of the inner ring which show that this ring has rotated on the axis and therefore was not mounted sufficiently tight. Unfortunately, no pictures are available for this damaged bearing.

Table 1 gives the considered frequency band for the considered outer race fault, allowing 2% variations for ball-pass frequency -outer race (BPFO). The value for the BPFO was obtained using the kinematics of the system and the known rotating speed of the shaft. When analysing the electrical quantities the modulations will be expected in the left and right bands with respect to the fundamental frequency. Indeed, the bands of interest for the electrical quantities are located at B l = f 0 -BPFOband and B r = f 0 + BPFOband.

Mechanical fault detection using electrical quantities

The computation of mechanical fault indicators is based on the estimated PSDs. For the considered experimental data the PSDs estimations are obtained with Welch averaged periodograms using a Hanning window of 2 [START_REF] El Bouchikhi | Stator current demodulation for induction machine rotor faults diagnosis[END_REF] samples, leading to a spectral resolution of approximately 0.95 Hz. Indeed, the spectral resolution is rather poor. However, it was chosen accordingly because the fault indicator is computed over a wide frequency band and there is not a real interest in precisely detecting any given peak.

Figure 4 depicts a zoom in on the frequency bands of interest (the left-hand side band B l and the right-hand side band B r ) in the PSDs of the current signal on phase 1 and the positive-sequence ISC. The figure shows that i + (t) has a better signal-to-noise ratio than the current i 1 (t). Also, after 350 hours into the experiment new peaks appear in these bands. This can be clearly seen in the PSD of i + (t) and it is less clear for i 1 (t).

Nonetheless, one of the datasets acquired after the 350 hours does not present any new peaks. The PSDs of the other two current signals are similar to that of i 1 (t). Based on the PSDs of the electrical signals in the frequency bands of interest, the mechanical fault indicators were computed. The indicators were computed for both currents and voltages, for the positive-sequence ISCs as well as for the phase signals by applying the same algorithm. This approach allows the comparison in terms of detection capabilities between single-phase and three-phase methods. For all indicators, the healthy case estimation S h was considered to correspond to the first dataset acquired.

Fault indicators based on signal energy

Figure 5 depicts the results obtained for the first mechanical fault indicator denoted m 1 and expressed in (3). There are several aspects highlighted in this figure. Firstly, the indicators computed using positive-sequence ISCs provide better results than the ones using the single-phase quantities. The higher amplitudes of the indicator towards the end of the experiment are more clearly distinguishable from the lower values at the beginning. Secondly, the voltage signals provide better results than the current signals. This might be explained by the fact that the voltage signals have higher amplitudes than the currents around the fundamental, thus the amplitudes of the modulations are also higher. Another aspect contributing to this result is that for this test bench the generator output is not directly connected to the grid, thus its voltage amplitude and fundamental frequency is allowed to vary and are not imposed by the grid. Regarding the absence of the bearing fault signature in the signals acquired right before 800 hours, one possible reason could be the fact that the bearings were not tightly fixed. In this case the fault position might change with respect to the load area and this might lead to a difference or temporary absence of the signature.

All in all, using the indicator m 1 computed for v + (t) and by setting an correct threshold the signature of the mechanical fault can be detected from the beginning of its apparition. However, this indicator performs rather poorly when applied to all other signals. This was expected considering the wide band (2 times 8.92 Hz) over which it was computed. The contribution added by the two new peaks is small compared to the total sum over the ≈ 18 Hz.

The poor performance of the first indicator justifies the need for improvement, thus the (b) Fault indicator using the voltages 5). The order of the median filter used to estimate the noise is set as five times the spectral resolution. Figure 6 explains how this indicator is computed by depicting the right-hand side modulation band B r for the first and last i + (t). In this figure the median for each of these two PSDs is also depicted while the gray area indicates the quantities used to compute the indicator m 2 .

For the first dataset, also considered healthy, the gray area is small. For the last dataset a new peak is visible and the gray area under it is large compared to the healthy case. Figure 7 shows the mechanical fault indicator m 2 . The indicator computed using singlephase current signals is still not capable to clearly separate the values before and after the fault. This result is expected to be poor, considering for example the PSD of i 1 (t) presented in Figure 4 which shows that the phase signal has a poorer SNR than i + (t). More so, the modulations in the left-hand side band of the currents PSDs are not visible for most of the single-phase signals. The results provided by this indicator enables the detention of the fault starting before 400 hours using all voltage signals and the positivesequence ISC for the currents.

Fault indicators based on frequency values

Figure 8 shows the frequencies for the maximum difference between each given PSD and the one that was estimated for the first dataset considered healthy. These frequency values pairs correspond to f ml and f mr from Equations (6). At the beginning of the experiment these values vary all over the frequency band which corresponds to the bearing outer race fault. After 350 hours the left and right frequency value both converge towards the same value for all the voltages and for i + (t). This suggests that the actual modulating frequency is between 221 and 222 Hz, and not precisely equal to the theoretical value of 223 Hz. (b) Frequencies f ml and f mr for the voltages Figure 9 shows the indicator m 3 obtained using the two above frequencies. If the value of this indicator is lower than the spectral resolution of 0.95 Hz the results suggests that there is a modulation of the fundamental frequency present in the frequency bands corresponding to BPFO. Thus this indicator is able to detect the appearance of the modulation using all three phase voltage signals as well as v + (t). Regarding the currents, the indicator can detect the presence of the modulation using i + (t) but the fault is still not properly detected using the single-phase currents. If only random noise is present in the corresponding bands the current algorithm still returns a frequency value for each band. Thus the probability of false alarm is rather high in this case as randomly the maximum difference between the noises in the bands can falsely indicate a modulation as is the case for the value for i + (t) right after 200 hours. This indicator can be improved by only providing values for f ml and f mr when peaks are present.

Conclusions

In this paper a method for condition monitoring of mechanical components has been proposed. The method relies on the use of three-phase signals and more specifically their positive-sequence instantaneous symmetrical component. Using this quantity two kinds of mechanical fault indicators have been proposed based on the signal energy and on the frequency values of components located in the frequency bands corresponding to the faults. The proposed method has been validated using experimental results. Throughout the presentation of the experimental results the three-phase approach has been compared to the single-phase one showing that the use of the positive-sequence ISC is justified by its better performance in detecting mechanical faults. Indeed, the proposed mechanical fault indicators computed using the positive-sequence ISC were able to detect the outer race bearing fault that generated high-frequency modulations.

As future developments of this work, the mechanical fault indicator that relies on the frequency values can be further improved. By only computing the modulating frequency value for detected peaks the number of false alarms triggered by this indicator can be reduced.

Figure 1 :

 1 Figure 1: Frequency modulations in ISCs due to mechanical faults

Figure 3 :

 3 Figure 3: Structure of the algorithm for high-frequency mechanical fault indicators computation

Figure 4 :

 4 Figure 4: PSDs of the current signals

Figure 5 :

 5 Figure 5: Mechanical fault indicator m 1 considering both left and right fault caracteristic frequency bands

  PSD of the last dataset

Figure 6 :

 6 Figure 6: Right-hand side modulation band for i + (t)

  Fault indicator using the voltages

Figure 7 :

 7 Figure 7: Mechanical fault indicator m 2

Figure 8 :

 8 Figure 8: Frequencies for the maximum difference between the PSDs

  Fault indicator using the voltages

Figure 9 :

 9 Figure 9: Mechanical fault indicator m 3

Table 1 :

 1 Fault characteristic frequency bands for Exp. B

	LSS speed [rpm] BPFO [Hz] BPFO ±2% band [Hz] 20 223.03 [218.57 227.49]

Acknowledgements

This research has been partly supported by KIC InnoEnergy, a company supported by the European Institute of Innovation and Technology (EIT), through KAStrion European project.

The authors would like to thank CETIM (Centre Technique des Industries Mécaniques) for providing the test-bench and experimental data.