Modèles de fiabilité et de maintenance prédictive de systèmes sujets à des défaillances interactives

RÉSUMÉ: L’interaction des défaillances est une thématique qui prend une ampleur considérable dans le monde de la recherche industrielle moderne. Les systèmes sont de plus en plus complexes et leurs fonctionnements et défaillances sur le long terme sont sujets à diverses sources d’influence internes et externes. Les actifs physiques en particulier sont soumis à l’impact du temps, de l’environnement et du rythme de leur utilisation. Connaître ces sources d’influence n’est pas suffisant car il importe de comprendre quelles sont les relations qui les lient afin de planifier de façon efficiente la maintenance des actifs. En effet, cette dernière peut s’avérer très couteuse et sa mauvaise planification peut conduire à l’utilisation de systèmes dangereux pouvant engendrer des évènements catastrophiques. La fiabilité est un vaste domaine. Elle propose une large panoplie de modèles mathématiques qui permettent de prédire le fonctionnement et les défaillances des actifs physiques. Ceci dit, les concepts des modèles les plus appliqués à ce jour se basent sur des hypothèses parfois simplistes et occultent bien souvent certaines relations de dépendances qui régissent un système. L’interaction des défaillances dans le cadre des dépendances stochastiques est abordée par de nombreux travaux de recherches. Par contre, la compréhension et l’implémentation de ces travaux demeurent un défi pour les spécialistes en maintenance qui ont besoin de modèles réalistes pour une maintenance préventive efficace. Cette thèse traite de la fiabilité et la maintenance prédictive des actifs physiques en exploitation et sujets à divers modes de défaillance interactifs. Elle établit avant tout l’importance d’accorder une attention particulière à l’interaction des défaillances dans le domaine de la fiabilité et de la maintenance. Dans une revue de littérature, les concepts et les méthodes de modélisation et d’optimisation en fiabilité et en maintenance préventive sont présentés. Les divers types de dépendances dans un système sont discutés. Un cas d’application, à savoir celui des ponceaux en béton, est proposé. Les travaux entrepris par la suite fournissent avant tout un cadre pour la modélisation de la fiabilité incluant l’interaction des défaillances. A cette fin, une étude comparative des modèles existants les plus pertinents est effectuée de points de vue conceptuel, méthodologique et applicatif. Le cadre étant défini, un modèle basé sur les chocs extrêmes et les chaînes de Markov est construit afin de valoriser le caractère séquentiel des défaillances interactives. Cette proposition est améliorée pour prendre en compte la dégradation du système. Une stratégie de maintenance prédictive est conséquemment développée. Toutes ces approches sont appliquées à un ensemble de ponceaux en béton observés sur plusieurs années. Cela permet d’expliquer les dépendances entre l’occurrence de déplacements et l’occurrence de fissures dans une structure. Tous ces concepts et résultats sont finalement discutés afin de déterminer des perspectives réalistes pour une étude approfondie de l’interactivité d’un point de vue fiabiliste et dans un but stratégique pour la planification de la maintenance.----------ABSTRACT: Failure interaction is a subject gaining growing attention in the world of modern industrial research. Systems are becoming increasingly complex. Their life cycles are subject to various internal and external influences. Physical assets in particular are impacted by time, environment and usage. Knowing these sources of influence is not enough. Indeed, it is important to understand the relationships between them in order to plan effectively for the maintenance of assets. Maintenance can be quite expensive. Thus, poor planning can lead to dangerous systems that could cause catastrophic events. Reliability engineering offers a wide range of mathematical models to predict failures. That being said, the concepts of the most widely applied models in the industry are often based on simplistic assumptions and tend to overlook certain dependencies within a system. Failure interaction in the context of stochastic dependencies is largely addressed in the literature. However, understanding and implementing the proposed approaches remains a challenge for maintenance specialists that need realistic models for efficient maintenance planning. This thesis focuses on the reliability and predictive maintenance of physical assets subject to interactive failure modes. First of all, it emphasizes the importance of paying particular attention to failure interaction. In a literature review, the concepts and methods for modeling and optimizing reliability and preventive maintenance are presented. The diverse dependencies in a system are discussed. A case study is proposed, namely concrete culverts. Subsequently, the research provides a framework for modeling reliability that integrates the interaction of failures. To this end, the most relevant models in the literature are comparatively studied from a conceptual, methodological and applicative point of view. In the defined framework, a model based on extreme shocks and Markov processes is built in order to represent the sequential nature of interactive failures. This approach is extended to take into account the natural degradation of a system. A predictive maintenance strategy is consequently developed. All these models are applied to a set of concrete culverts observed over several years. The dependences between the occurrence of displacements and the occurrence of cracks in a structure are explained through these approaches. Finally, these concepts and results are discussed in order to determine realistic perspectives for in-depth studies of the impact of failure interaction on reliability and for strategic maintenance plannin

Development of a Condition Monitoring System for an Articulated Wave Energy Converter

ESREL 2016: European Safety and Reliability Conference 2016, Glasgow, UK, 25-29 September, 2016This is the author accepted manuscript. The final version is available from the publisher.Condition monitoring systems (CMS) in renewable energy devices allow for the detection of oncoming faults, providing data to undertake pre-emptive maintenance. By defining a systems functional re-quirements and identifying of critical failure modes, proactive maintenance strategies to be produced. The lack of operational data in the marine energy industry, and lack of consensus in operating principles between devic-es, means that a non-standardised CMS package is available for wave energy converters (WECs). In this study a Failure Modes and Effects Analysis (FMEA) is undertaken in order to identify the critical failure modes of an articulated WEC, measurement priorities are identified and a set of monitoring solutions provided. Installing a CMS provides the framework for collecting quality component reliability data, however further development is required for building a proactive maintenance strategy and for continuous reliability improvement

A condition-based opportunistic maintenance policy integrated with energy efficiency for two-component parallel systems

Purpose: In order to improve the energy utilization and achieve sustainable development, this paper integrates energy efficiency into condition-based maintenance(CBM) decision-making for two-component parallel systems. The objective is to obtain the optimal maintenance policy by minimizing total cost. Design/methodology/approach: Based on energy efficiency, the paper considers the economic dependence between the two components to take opportunistic maintenance. Specifically, the objective function consists of traditional maintenance cost and energy cost incurred by energy consumption of components. In order to assess the performance of the proposed new maintenance policy, the paper uses Monte-Carlo method to evaluate the total cost and find the optimal maintenance policy. Findings: Simulation results indicate that the new maintenance policy is superior to the classical condition-based opportunistic maintenance policy in terms of total economic costs. Originality/value: For two-component parallel systems, previous researches usually simply establish a condition-based opportunistic maintenance model based on real deterioration data, but ignore energy consumption, energy efficiency (EE) and their contributions of sustainable development. This paper creatively takes energy efficiency into condition-based maintenance(CBM) decision-making process, and proposes a new condition-based opportunistic maintenance policy by using energy efficiency indicator(EEI).Peer Reviewe

Integrated optimization of maintenance interventions and spare part selection for a partially observable multi-component system

Advanced technical systems are typically composed of multiple critical components whose failure cause a system failure. Often, it is not technically or economically possible to install sensors dedicated to each component, which means that the exact condition of each component cannot be monitored, but a system level failure or defect can be observed. The service provider then needs to implement a condition based maintenance policy that is based on partial information on the systems condition. Furthermore, when the service provider decides to service the system, (s)he also needs to decide which spare part(s) to bring along in order to avoid emergency shipments and part returns. We model this problem as an infinite horizon partially observable Markov decision process. In a set of numerical experiments, we first compare the optimal policy with preventive and corrective maintenance policies: The optimal policy leads on average to a 28% and 15% cost decrease, respectively. Second, we investigate the value of having full information, i.e., sensors dedicated to each component: This leads on average to a 13% cost decrease compared to the case with partial information. Interestingly, having full information is more valuable for cheaper, less reliable components than for more expensive, more reliable components

Risks

This book is a collection of feature articles published in Risks in 2020. They were all written by experts in their respective fields. In these articles, they all develop and present new aspects and insights that can help us to understand and cope with the different and ever-changing aspects of risks. In some of the feature articles the probabilistic risk modeling is the central focus, whereas impact and innovation, in the context of financial economics and actuarial science, is somewhat retained and left for future research. In other articles it is the other way around. Ideas and perceptions in financial markets are the driving force of the research but they do not necessarily rely on innovation in the underlying risk models. Together, they are state-of-the-art, expert-led, up-to-date contributions, demonstrating what Risks is and what Risks has to offer: articles that focus on the central aspects of insurance and financial risk management, that detail progress and paths of further development in understanding and dealing with...risks. Asking the same type of questions (which risk allocation and mitigation should be provided, and why?) creates value from three different perspectives: the normative perspective of market regulator; the existential perspective of the financial institution; the phenomenological perspective of the individual consumer or policy holder

Maintenance policies considering degradation and cost processes for a multicomponent system

Purpose (limit 100 words) This paper models the deterioration process of a multi-component system. Each deterioration process is modelled by the Wiener process. Design/methodology/approach (limit 100 words) Condition-based maintenance (CbM) is a method for reducing the probability of system failures as well as the operating cost. Nowadays, a system is composed of multiple components. If the deteriorating process of each component can be monitored and then modelled by a stochastic process, the deteriorating process of the system is a stochastic process. The cost of repairing failures of the components in the system forms a stochastic process as well, and is known as a cost process. Findings (limit 100 words) When a linear combination of the processes, which can be the deterioration processes and the cost processes, exceeds a pre-specified threshold, a replacement policy will be carried out to preventively maintain the system. Originality/value (limit 100 words) Under this setting, this paper investigates maintenance policies based on the deterioration process and the cost process. Numerical examples are given to illustrate the optimisation process

Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency

Many production facilities consist of multiple and functionally exchangeable units of equipment, such as pumps or turbines, that are jointly used to satisfy a given production target. Such systems often have to ensure high levels of reliability and availability. The deterioration rates of the units typically depend on their production rates, implying that the operator can control deterioration by dynamically reallocating load among units. In this study, we examine the value of condition-based load-sharing decisions for two-unit systems with economic dependency. We formulate the system as a Markov decision process and provide optimal joint condition-based maintenance and production policies. Our numerical results show that, dependent on the system characteristics, substantial cost savings of up to 40% can be realized compared to the optimal condition-based maintenance policy under equal load-sharing. The structure of the optimal policy particularly depends on the maintenance setup cost and the penalty that is incurred if the production target is not satisfied. For systems with high setup costs, the clustering of maintenance interventions is improved by synchronizing the deterioration of the units. On the contrary, for low setup costs, the deterioration levels are desynchronized and the maintenance interventions are alternated

Modeling multivariate degradation processes with time‐variant covariates and imperfect maintenance effects

International audienceThis article proposes two types of degradation models that are suitable for describing multivariate degrading systems subject to time‐variant covariates and imperfect maintenance activities. A multivariate Wiener process is constructed as a baseline model, on top of which two types of models are developed to meaningfully characterize the time‐variant covariates and imperfect maintenance effects. The underlying difference between the two models lies in the way of capturing the influences of covariates and maintenance: The first model reflects these impacts in the degradation rates/paths directly, whereas the second one describes the impacts by modifying the time scales governing the degradation processes. In each model, two particular imperfect maintenance models are presented, which differ in the extent of reduction in degradation level or virtual age. The two degradation models are then compared in certain special cases. The proposed multivariate degradation models pertain to complex industrial systems whose health deterioration can be characterized by multiple performance characteristics and can be altered or affected by maintenance activities and operating/environmental conditions