Eric Levrat

Remaining useful life estimation based on stochastic deterioration models: A comparative study

Prognostic of system lifetime is a basic requirement for condition-based maintenance in many application domains where safety, reliability, and availability are considered of first importance. This paper presents a probabilistic method for prognostic applied to the 2008 PHM Conference Challenge data. A stochastic process (Wiener process) combined with a data analysis method (Principal Component Analysis) is proposed to model the deterioration of the components and to estimate the RUL on a case study. The advantages of our probabilistic approach are pointed out and a comparison with existing results on the same data is made.

Subjective evaluation of car seat comfort with fuzzy set techniques

Subjective evaluation is a central problem in the life cycle of most products. Automotive companies use subjective evaluation of car seat comfort to check the adequacy between specifications and effective realization. A great deal of time and money are spent to achieve this goal. To solve these problems, our projects goal is to save the expert knowledge and to automate this phase in order to achieve more flexibility. This article presents some of the first steps in saving the expert know‐how in addition to help for decision making. We propose tools for the expert assessment acquisition and processing using a fuzzy set technique called expertons. Expertons are applied to a typical example related to our application. It emphasizes the suitability of expertons for the help for decision making.

E-maintenance: review and conceptual framework

E-maintenance is an emerging concept generally defined as ‘a maintenance management concept whereby assets are monitored and managed over the Internet’. Nevertheless a lot of complementary definitions exist in which are introduced the principles of collaboration, knowledge, intelligence, etc. There is no consensus and the number of references and work is huge1 without a unique repository to ensure consistency. Consequently the aim of this research and review note is to define more precisely the emerging concept of e-maintenance and then to propose and discuss a conceptual e-maintenance framework based on a Zachman framework. Such a framework can facilitate a widespread understanding of e-maintenance and provide useful guidance for supporting e-maintenance deployment through services, processes, organisation and infrastructure. It should serve as a reference for an inventory on all the work related to this topic.

Conceptual framework for e-Maintenance: Illustration by e-Maintenance technologies and platforms☆

Abstract At present we can find different complementary definitions of the term e-Maintenance. These definitions apply to maintenance several principles and concepts such as collaboration, pro-activity, knowledge, intelligence, web services or the Internet. A clear consensus is not yet reached, even when some contributions try to propose unique repositories to ensure consistency. Consequently the aim of this paper is: (a) to discuss, briefly, on the concept of e-Maintenance and on a first conceptual e-Maintenance framework based on 5 abstraction levels in order; (b) to detail the last level named “infrastructure” for illustrating e-Maintenance technologies and platforms. This level allows to put in evidence new technologies supporting e-Maintenance services and to describe e-Maintenance architecture resulting from the technologys integration. The main illustration is done with a TELeMAintenance platform (TELMA).

A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions

This paper deals with a proactive condition-based maintenance (CBM) considering both perfect and imperfect maintenance actions for a deteriorating system. Perfect maintenance actions restore completely the system to the ‘as good as new’ state. Their related cost are however often high. The first objective of the paper is to investigate the impacts of imperfect maintenance actions. In fact, both positive and negative impacts are considered. Positive impact means that the imperfect maintenance cost is usually low. Negative impact implies that (i) the imperfect maintenance restores a system to a state between good-as-new and bad-as-old and (ii) each imperfect preventive action may accelerate the speed of the system׳s deterioration process. The second objective of the paper is to propose an adaptive maintenance policy which can help to select optimally maintenance actions (perfect or imperfect actions), if needed, at each inspection time. Moreover, the time interval between two successive inspection points is determined according to a remaining useful life (RUL) based-inspection policy. To illustrate the use of the proposed maintenance policy, a numerical example finally is introduced.

Bayesian Networks and Evidence Theory to Model Complex Systems Reliability

This paper deals with the use of Bayesian Networks to compute system reliability of complex systems under epistemic uncertainty. In the context of incompleteness of reliability data and inconsistencies between the reliability model and the system modeled, the evidence theory is more suitable to manage this epistemic uncertainty. We propose to adapt the Bayesian Network model of reliability in order to integrate the evidence theory and then to produce an Evidential Network. Three examples are proposed to observe the propagation mechanism of the uncertainty through the network and its influence on the system reliability.

Generic prognosis model for proactive maintenance decision support: application to pre-industrial e-maintenance test bed

Proactivity in maintenance, which is mainly materialized by degradation-based anticipation, becomes essential to avoid failure situation with negative impact on product and/or system conditions. It leads to make emerging the E-maintenance philosophy to move from “fail and fix” maintenance practices to “predict and prevent” strategies. Within these new strategies, the anticipation action is fully supported by prognosis business process. Indeed it analyses the degradation impact on the component itself but also on the global performances of the production system in order to predict future failures of the system and investigate (future maintenance) actions. However, only few research works focuses on generic and scalable prognostic approach. Existing methods are generally restricted on component view and for solving the failure prediction issue. Consequently, the contribution presented in this paper aims at developing a global formalization of the generic prognosis business process. This generic process can be used after, from an instantiation procedure, to develop specific prognosis processes related to particular application such as shown in this paper with the case of E-maintenance platform developed within DYNAMITE Project.

Odds-based decision-making tool for opportunistic production-maintenance synchronization

The importance of the maintenance function has increased because of its role in keeping and improving system availability and safety, as well as product quality. Indeed a new role for maintenance exists to enhance the eco-efficiency of the product lifecycle. The concept of ‘lifecycle maintenance’ emerged to stress this role leading to promote, at the manufacturing stage, an innovative culture wherein maintenance activities become of equal importance to actual production activities. This equivalence requires mainly considering the integration of the maintenance and the production strategy planning for developing opportunistic maintenance tasks preserving conjointly the product–production–equipment performances. In this paper, a novel approach is proposed for integrating maintenance into production planning. The approach uses the ‘odds algorithm’ and is based upon the theory of optimal stopping. The objective is to select, among all the production stoppages already planned, those which will be optimal to develop maintenance tasks preserving the expected product conditions. It combines criteria such as product performance and component reliability. The feasibility and benefits of this approach are investigated first from a numerical point of view and then from an industrial point of view using TELMA (TÉLé-MAintenance) platform supporting the unwinding of metal bobbins.

Weights determination of OWA operators by parametric identification

This contribution presents a new approach on weights determination in industrial decision making aided by OWA operators. Multi-criteria decision aid is a good way, for an industrialists, to determine his preferred compromise products, in the case of risk products or innovative products. The multi-criteria decision support chosen is the Ordered Weighted Average (OWA) operators, introduced by Yager [R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Trans. Syst. Man Cybern. 18 (1988) 183-190]. The interest of this aggregation method is, beyond its simplicity of use, product evaluation according unique scale. Furthermore, the weights are not fixed by criterion but according to utility level. First, a learning sample is ranked by the decision-maker. Then, this ranked sample is used in order to determine the weights by parametric identification. For this, an hypothesis of equipartition of the scores of each sample is used. An industrial application, from a food production, illustrates this approach. The ranks obtained from several samples are compared.

Methodological developments for probabilistic risk analyses of socio-technical systems

Nowadays, the risk analysis of critical systems cannot be focused only on a technical dimension. Indeed, well-known accidents in nuclear or aerospace areas underlined initiating causes also related to technical and organizational viewpoints. This led to the development of methods for risk assessment considering three main aspects on the system resources: the technical process, the operator constraining the process, and the organization constraining human actions on the process. However, only few scientific works have tried to join these methods in a unique and global approach. Thus this paper focuses on a methodology that aims to achieve the integration of the different methods in order to assess the risks probabilistically. The integration is based on (a) system knowledge structuring and (b) its unified modelling by means of Bayesian networks also supporting quantification and simulation phases. The methodology is applied to an industrial case to show its feasibility and to draw conclusions regarding the model relevance for system risk analysis. The results of the methodology can be used by decision makers to prioritize their actions when faced with potential or real risks.