Fuzzy Reliability Assessment of Systems with Multiple Dependent Competing Degradation Processes

International audienceComponents are often subject to multiple competing degradation processes. For multi-component systems, the degradation dependency within one component or/and among components need to be considered. Physics-based models (PBMs) and multi-state models (MSMs) are often used for component degradation processes, particularly when statistical data are limited. In this paper, we treat dependencies between degradation processes within a piecewise-deterministic Markov process (PDMP) modeling framework. Epistemic (subjective) uncertainty can arise due to the incomplete or imprecise knowledge about the degradation processes and the governing parameters: to take into account this, we describe the parameters of the PDMP model as fuzzy numbers. Then, we extend the finite-volume (FV) method to quantify the (fuzzy) reliability of the system. The proposed method is tested on one subsystem of the residual heat removal system (RHRS) of a nuclear power plant, and a comparison is offered with a Monte Carlo (MC) simulation solution: the results show that our method can be most efficient

A Condition-Based Maintenance Model for Assets with Accelerated Deterioration Due to Fault Propagation

Complex industrial assets such as power transformers are subject to accelerated deterioration when one of its constituent component malfunctions, affecting the condition of other components, which is a phenomenon called fault propagation. In this paper, we present a novel approach for optimizing condition-based maintenance policies for such assets by modelling their deterioration as a multiple dependent deterioration path process. The aim of the policy is to replace the malfunctioned component and mitigate accelerated deterioration at minimal impact to the business. The maintenance model provides guidance on determining inspection and maintenance strategies to optimize asset availability and operational cost.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TR.2015.243913

Security-constrained unit commitment problem with transmission switching reliability and dynamic thermal line rating

In security-constrained unit commitment (SCUC) problems, one approach to decrease operation costs is using a transmission switching (TS) tool. In SCUC problems with TS, one of the main challenges is that there is no limitation for the number of switching of circuit breakers (CB) in the system. In this article, the reliability of CB is merged into the SCUC problem with the TS and is considered as a limiting factor for switching. With a more reliable CB, the overall reliability of the system will be increased. So, it can be concluded that the reliability of a CB affects the amount of load shedding. Reliability of a CB is a nonlinear equation based on the number of switching in a period. An approach is presented to linearize the switch reliability equation. In this article, the power flow model uses an improved linear ac optimal power flow and a dynamic thermal line rating (DTLR) model, which considers the weather conditions. Other than CB reliability, DTLR in SCUC problems affects the number of switching and, as a result, operation costs will be significantly decreased. The proposed model is empowered by Bender's decomposition and is tested on 6-bus and 118-bus IEEE test systems.fi=vertaisarvioitu|en=peerReviewed

A COMPARATIVE ANALYSIS OF METAHEURISTIC MAINTENANCE OPTIMIZATION OF REFUSE COLLECTION VEHICLES USING THE TAGUCHI EXPERIMENTAL DESIGN

In this paper, a comparative analysis of the metaheuristic maintenance optimization of refuse collection vehicles (RCV) using the Taguchi experimental design is presented based on a RCV model as a multi-state degradation system with two dependent subsystems. The model which is based on a probabilistic approach includes two stochastic degradation processes, a random failure process and a set of maintenance actions and their effects. The optimal values of the mean time to preventive maintenance are determined by maximizing the availability of the complete system and by minimizing total costs. In order to solve the real life problem of the multi-objective optimization of RCV maintenance, three different metaheuristic optimization algorithms were used: a real coded genetic algorithm, an improved harmony search algorithm and simulated annealing. Each algorithm has parameters that need to be accurately calibrated to ensure the best performance. For this purpose, calibration was applied to the parameters by means of the Taguchi method. Finally, the optimal values of the mean time to minimal preventive maintenance of RCVs are obtained and computational results of the three optimization algorithms are compared

Un cadre holistique de la modélisation de la dégradation pour l’analyse de fiabilité et optimisation de la maintenance de systèmes de sécurité nucléaires

Components of nuclear safety systems are in general highly reliable, which leads to a difficulty in modeling their degradation and failure behaviors due to the limited amount of data available. Besides, the complexity of such modeling task is increased by the fact that these systems are often subject to multiple competing degradation processes and that these can be dependent under certain circumstances, and influenced by a number of external factors (e.g. temperature, stress, mechanical shocks, etc.). In this complicated problem setting, this PhD work aims to develop a holistic framework of models and computational methods for the reliability-based analysis and maintenance optimization of nuclear safety systems taking into account the available knowledge on the systems, degradation and failure behaviors, their dependencies, the external influencing factors and the associated uncertainties.The original scientific contributions of the work are: (1) For single components, we integrate random shocks into multi-state physics models for component reliability analysis, considering general dependencies between the degradation and two types of random shocks. (2) For multi-component systems (with a limited number of components):(a) a piecewise-deterministic Markov process modeling framework is developed to treat degradation dependency in a system whose degradation processes are modeled by physics-based models and multi-state models; (b) epistemic uncertainty due to incomplete or imprecise knowledge is considered and a finite-volume scheme is extended to assess the (fuzzy) system reliability; (c) the mean absolute deviation importance measures are extended for components with multiple dependent competing degradation processes and subject to maintenance; (d) the optimal maintenance policy considering epistemic uncertainty and degradation dependency is derived by combining finite-volume scheme, differential evolution and non-dominated sorting differential evolution; (e) the modeling framework of (a) is extended by including the impacts of random shocks on the dependent degradation processes.(3) For multi-component systems (with a large number of components), a reliability assessment method is proposed considering degradation dependency, by combining binary decision diagrams and Monte Carlo simulation to reduce computational costs.Composants de systèmes de sûreté nucléaire sont en général très fiable, ce qui conduit à une difficulté de modéliser leurs comportements de dégradation et d'échec en raison de la quantité limitée de données disponibles. Par ailleurs, la complexité de cette tâche de modélisation est augmentée par le fait que ces systèmes sont souvent l'objet de multiples processus concurrents de dégradation et que ceux-ci peut être dépendants dans certaines circonstances, et influencé par un certain nombre de facteurs externes (par exemple la température, le stress, les chocs mécaniques, etc.).Dans ce cadre de problème compliqué, ce travail de thèse vise à développer un cadre holistique de modèles et de méthodes de calcul pour l'analyse basée sur la fiabilité et la maintenance d'optimisation des systèmes de sûreté nucléaire en tenant compte des connaissances disponibles sur les systèmes, les comportements de dégradation et de défaillance, de leurs dépendances, les facteurs influençant externes et les incertitudes associées.Les contributions scientifiques originales dans la thèse sont:(1) Pour les composants simples, nous intégrons des chocs aléatoires dans les modèles de physique multi-états pour l'analyse de la fiabilité des composants qui envisagent dépendances générales entre la dégradation et de deux types de chocs aléatoires.(2) Pour les systèmes multi-composants (avec un nombre limité de composants):(a) un cadre de modélisation de processus de Markov déterministes par morceaux est développé pour traiter la dépendance de dégradation dans un système dont les processus de dégradation sont modélisées par des modèles basés sur la physique et des modèles multi-états; (b) l'incertitude épistémique à cause de la connaissance incomplète ou imprécise est considéré et une méthode volumes finis est prolongée pour évaluer la fiabilité (floue) du système; (c) les mesures d'importance de l'écart moyen absolu sont étendues pour les composants avec multiples processus concurrents dépendants de dégradation et soumis à l'entretien; (d) la politique optimale de maintenance compte tenu de l'incertitude épistémique et la dépendance de dégradation est dérivé en combinant schéma volumes finis, évolution différentielle et non-dominée de tri évolution différentielle; (e) le cadre de la modélisation de (a) est étendu en incluant les impacts des chocs aléatoires sur les processus dépendants de dégradation.(3) Pour les systèmes multi-composants (avec un grand nombre de composants), une méthode d'évaluation de la fiabilité est proposé considérant la dépendance dégradation en combinant des diagrammes de décision binaires et simulation de Monte Carlo pour réduire le coût de calcul

Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model

For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models. In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, δ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks. Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o

Inspection and replacement models for reliability and maintenance: filling in gaps

A thesis submitted in fulfillment of the requirements for the Degree of Doctor of Philosophy, School of Statistics and Actuarial Science, Faculty of Science University of Witwatersrand, Johannesburg. February 2017.The work done in this thesis on finite planning horizon inspection models has demonstrated that with the advent of powerful computers these days it is possible to easily find an optimal inspection schedule when the lifetime distribution is known. For the case of system time to failure following a uniform distribution, a result for the maximum number of inspections for the finite planning models has been derived. If the time to failure follows an exponential distribution, it has been noted that periodically carrying out inspections may not result in maximization of expected profit. For the Weibull distributions family (of which the exponential distribution is a special case), evenly spreading the inspections over a given finite planning horizon may not lead to any serious prejudice in profit. The case of inspection models where inspections are of non-negligible duration has also been explored. The conditions necessary for inspections that are evenly spread over the entire planning horizon to be near-optimal when system time to failure either follows a uniform distribution or exponential distribution have been explored. Finite and infinite planning horizon models where inspections are imperfect have been researched on. Interesting observations on the impact of Type I and Type II errors in inspection have been made. These observations are listed on page 174. A clear and easy to implement road map on how to get an optimal inspection permutation in problems first discussed by Zuckerman (1989) and later reviewed by Qiu (1991) for both the undiscounted and discounted cases has been given. The only challenge envisaged when a system has a large number of components is that of computer memory requirements - which nowadays is fast being overcome. In particular, it has been clearly demonstrated that the impact of repair times and per unit of time repair costs on the optimal inspection permutation cannot be ignored. The ideas and procedures of determining optimal inspection permutations which have been developed in this thesis will no doubt lead to huge cost savings especially for systems where the cost of inspecting components is huge.XL201

AGENT AUTONOMY APPROACH TO PROBABILISTIC PHYSICS-OF-FAILURE MODELING OF COMPLEX DYNAMIC SYSTEMS WITH INTERACTING FAILURE MECHANISMS

A novel computational and inference framework of the physics-of-failure (PoF) reliability modeling for complex dynamic systems has been established in this research. The PoF-based reliability models are used to perform a real time simulation of system failure processes, so that the system level reliability modeling would constitute inferences from checking the status of component level reliability at any given time. The "agent autonomy" concept is applied as a solution method for the system-level probabilistic PoF-based (i.e. PPoF-based) modeling. This concept originated from artificial intelligence (AI) as a leading intelligent computational inference in modeling of multi agents systems (MAS). The concept of agent autonomy in the context of reliability modeling was first proposed by M. Azarkhail [1], where a fundamentally new idea of system representation by autonomous intelligent agents for the purpose of reliability modeling was introduced. Contribution of the current work lies in the further development of the agent anatomy concept, particularly the refined agent classification within the scope of the PoF-based system reliability modeling, new approaches to the learning and the autonomy properties of the intelligent agents, and modeling interacting failure mechanisms within the dynamic engineering system. The autonomous property of intelligent agents is defined as agent's ability to self-activate, deactivate or completely redefine their role in the analysis. This property of agents and the ability to model interacting failure mechanisms of the system elements makes the agent autonomy fundamentally different from all existing methods of probabilistic PoF-based reliability modeling. 1. Azarkhail, M., "Agent Autonomy Approach to Physics-Based Reliability Modeling of Structures and Mechanical Systems", PhD thesis, University of Maryland, College Park, 2007

Methods for modeling degradation of electrical engineering components for lifetime prognosis

Reliability of electrical components is an issue studied to improve the quality of products, and to plan maintenance in case of failure. Reliability is measured by studying the causes of failure and the mean time to failure. One of the methods applied in this field is the study of component aging, because failure often occurs after degradation. The objective of this thesis is to model the degradation of components in electrical engineering, in order to estimate their lifetime. More specifically, this thesis will study large area organic white light sources (OLEDs). These sources offer several advantages in the world of lighting thanks to their thinness, their low energy consumption and their ability to adapt to a wide range of applications. The second components studied are electrical insulators applied to pairs of twisted copper wires, which are commonly used in low voltage electrical machines. First, the degradation and failure mechanisms of the various electrical components, including OLEDs and insulators, are studied. This is done to identify the operational stresses for including them in the aging model. After identifying the main causes of aging, general physical models are studied to quantify the effects of operational stresses. Empirical models are also presented when the physics of degradation is unknown or difficult to model. Next, methods for estimating the parameters of these models are presented, such as multilinear and nonlinear regression, as well as stochastic methods. Other methods based on artificial intelli­gence and online diagnosis are also presented, but they will not be studied in this thesis. These methods are applied to degradation data of organic LEDs and twisted pair insulators. For this purpose, accelerated and multifactor aging test benches are designed based on factorial experimental designs and response surface methods, in order to optimize the cost of the experiments. Then, a measurement protocol is described, in order to optimize the inspection time and to collect periodic data. Finally, estimation methods tackle unconstrained deterministic degradation models based on the measured data. The best empirical model of the degradation trajectory is then chosen based on model selection criteria. In a second step, the parameters of the degradation trajectories are modeled based on operational constraints. The parameters of the aging factors and their interactions are estimated by multilinear regression and according to different learning sets. The significance of the parameters is evaluated by statistical methods if possible. Finally, the lifetime of the experiments in the validation sets is predicted based on the parameters estimated by the different learning sets. The training set with the best lifetime prediction rate is considered the best