A unified methodology of maintenance management for repairable systems based on optimal stopping theory

This dissertation focuses on the study of maintenance management for repairable systems based on optimal stopping theory. From reliability engineering’s point of view, all systems are subject to deterioration with age and usage. System deterioration can take various forms, including wear, fatigue, fracture, cracking, breaking, corrosion, erosion and instability, any of which may ultimately cause the system to fail to perform its required function. Consequently, controlling system deterioration through maintenance and thus controlling the risk of system failure becomes beneficial or even necessary. Decision makers constantly face two fundamental problems with respect to system maintenance. One is whether or when preventive maintenance should be performed in order to avoid costly failures. The other problem is how to make the choice among different maintenance actions in response to a system failure. The whole purpose of maintenance management is to keep the system in good working condition at a reasonably low cost, thus the tradeoff between cost and condition plays a central role in the study of maintenance management, which demands rigorous optimization. The agenda of this research is to develop a unified methodology for modeling and optimization of maintenance systems. A general modeling framework with six classifying criteria is to be developed to formulate and analyze a wide range of maintenance systems which include many existing models in the literature. A unified optimization procedure is developed based on optimal stopping, semi-martingale, and lambda-maximization techniques to solve these models contained in the framework. A comprehensive model is proposed and solved in this general framework using the developed procedure which incorporates many other models as special cases. Policy comparison and policy optimality are studied to offer further insights. Along the theoretical development, numerical examples are provided to illustrate the applicability of the methodology. The main contribution of this research is that the unified modeling framework and systematic optimization procedure structurize the pool of models and policies, weed out non-optimal policies, and establish a theoretical foundation for further development

Developing Leading and Lagging Indicators to Enhance Equipment Reliability in a Lean System

With increasing complexity in equipment, the failure rates are becoming a critical metric due to the unplanned maintenance in a production environment. Unplanned maintenance in manufacturing process is created issues with downtimes and decreasing the reliability of equipment. Failures in equipment have resulted in the loss of revenue to organizations encouraging maintenance practitioners to analyze ways to change unplanned to planned maintenance. Efficient failure prediction models are being developed to learn about the failures in advance. With this information, failures predicted can reduce the downtimes in the system and improve the throughput. The goal of this thesis is to predict failure in centrifugal pumps using various machine learning models like random forest, stochastic gradient boosting, and extreme gradient boosting. For accurate prediction, historical sensor measurements were modified into leading and lagging indicators which explained the failure patterns in the equipment were developed. The best subset of indicators was selected by filtering using random forest and utilized in the developed model. Finally, the models give a probability of failure before the failure occurs. Appropriate evaluation metrics were used to obtain the accurate model. The proposed methodology was illustrated with two case studies: first, to the centrifugal pump asset performance data provided by Meridium, Inc. and second, the data collected from aircraft turbine engine provided in the NASA prognostics data repository. The automated methodology was shown to develop and identify appropriate failure leading and lagging indicators in both cases and facilitate machine learning model development

Modèle de réparation minimale basé sur l’actualisation bayésienne du taux de défaillance

RÉSUMÉ : L’estimation des paramètres de fiabilité d’un équipement est toujours conditionnée par la disponibilité des données à savoir les durées de vie et leurs natures complètes ou censurées ce qui rend cette tâche difficile. Les méthodes d’estimation de ces paramètres peuvent varier selon la situation. Cette estimation est une étape cruciale pour un fiabiliste pour être en mesure de proposer des stratégies de maintenance préventive de l’équipement et ainsi maximiser sa disponibilité et minimiser ses coûts de maintenance Notre mémoire focalise sur deux principaux objectifs: 1. Établir un modèle d’actualisation des paramètres du taux de défaillance d’un équipement en utilisant l’inférence bayésienne et les méthodes de simulation Chaines de Markov Monte Carlo (MCMC). 2. Proposer une stratégie de remplacement périodique avec réparation minimale (Remplacement du composant défaillant par un composant aussi mauvais que vieux) en cas de défaillance tenant compte du modèle d’actualisation bayésienne du taux de défaillance. La méthodologie suivie pour atteindre le premier objectif consiste à modéliser le taux de défaillance d’un équipement par une loi exponentielle. Ce taux de défaillance est actualisé par la prise en compte d’une distribution a priori représentant l’avis d’expert. Cette distribution est caractérisée par une loi normale. Comme cette loi est non conjuguée, la simulation MCMC est utilisée pour déterminer l’a posteriori du taux de défaillance. Cet a posteriori représente la valeur actualisée du taux de défaillance. Pour le second objectif, une modélisation analytique du coût total moyen de la stratégie de remplacement périodique avec réparation minimale en cas de défaillance est proposée. Cette modélisation prend en compte le taux de défaillance actualisée précédemment. Comme le modèle ne s’apprête pas à une dérivation analytique, une approche par simulation est considérée pour déterminer la stratégie optimale. Un cas d’étude est utilisé tout au long du mémoire pour valider les modèles proposés.----------ABSTRACT : The estimation of equipment reliability Parameters is always conditioned by the availability of its life-time data and the nature of this data such as complete or censored making this task delicate. The methods of estimation of these parameters may vary by situation. This estimation is a crucial step for a reliability engineer to propose strategies for preventive maintenance of equipment, maximizing availability and minimizing the costs of maintenance Our work focuses on two main objectives: 1. Establish a model that updates the equipment failure rate parameters by using Bayesian inference and simulation methods of Monte Carlo Markov Chains (MCMC). 2. Develop a minimal repair strategy, taking into account the Bayesian estimation model of updating the failure rate. The methodology used to achieve the first objective is to model the failure rate of equipment by an exponential law. This failure rate is updated by taking into account a representative expert advice prior. The prior is characterized by a normal distribution. As this law is non-conjugate, the MCMC simulation is used to determine the posterior failure rate. This posterior is the current value of the failure rate. For the second objective, an analytical model of the average total cost for the periodic replacement with minimal repair strategy in case of failure is proposed. This model takes into account the failure rate previously updated. As the model is not about to analytical derivation, a simulation approach is considered to determine the optimal strategy. A case study is used throughout the store to validate the proposed models

Warranty cost analysis under imperfect repair

Cataloged from PDF version of article.Increasing market competition forces manufacturers to offer extensive warranties. Faced with the challenge of keeping the associated costs under control, most companies seek efficient rectification strategies. In this study, we focus on the repair strategies with the intent of minimizing the manufacturer’s expected warranty cost expressed as a function of various parameters such as product reliability, structure of the cost function and the type of the warranty contract. We consider both one- and two-dimensional warranties, and use quasi renewal processes to model the product failures along with the associated repair actions. We propose static, improved and dynamic repair policies, and develop representative cost functions to evaluate the effectiveness of these alternative policies. We consider products with different reliability structures under the most commonly observed types of warranty contracts. Experimental results indicate that the dynamic policy generally outperforms both static and improved policies on highly reliable products, whereas the improved policy is the best performer for products with low reliability. Although, the increasing number of factors arising in the analysis of two-dimensional policies renders generalizations difficult, several insights can be offered for the selection of the rectification action based on empirical evidence.Samatlı, GülayM.S

Stratégie de maintenance optimale du parc de locomotives de Transrail

Transrail est la société qui exploite présentement les chemins de fer entre Dakar (au Sénégal) et Bamako (au Mali) une voie métrique longue de 1 288 kilomètres. Deux des particularités des systèmes de transport sont l'impossibilité de constituer un inventaire de voyages et la perte d'une partie ou de la totalité du temps de production consacré aux voyages durant lesquels l'outil de production tombe en panne avant d'arriver à destination; ce qui rend l'implantation d'une stratégie de maintenance d'autant plus importante dans ce cas. Il faut alors maintenir la disponibilité des engins de transport autour d'une valeur suffisante pour permettre au système de transport de répondre de manière satisfaisante à la demande. La problématique abordée par ce mémoire est l'intégration de la disponibilité de deux ressources de maintenance importantes que sont les équipements de rechange et les équipes de maintenances, ainsi que la prise en compte dans le modèle de maintenance préventive de plusieurs opérations d'importances différentes. Dans le but d'avoir des modèles de maintenance reflétant mieux la réalité par l'élimination de certaines hypothèses simplificatrices, ce mémoire utilise la simulation, la planification et l'analyse statistique d'expérience pour l'optimisation des stratégies de maintenance. Particulièrement, ce mémoire vise à trouver la stratégie de maintenance optimale pour le parc de locomotives de Transrail, en déterminant le type de stratégie de maintenance et les valeurs optimales des facteurs dont dépendent les mesures de performance. Dans un premier temps, les deux mesures de performance sont la disponibilité moyenne annuelle et le coût moyen annuel de maintenance et dans un deuxième temps, la mesure est la désirabilité globale obtenue en donnant différents poids à chacune de ces deux premières mesures. Pour ce faire, les quatre stratégies de maintenance retenues sont : la stratégie de maintenance de type âge, la stratégie de maintenance de type bloc, la stratégie de maintenance de type bloc étendu et une quatrième stratégie de maintenance obtenue en intégrant la disponibilité du personnel de maintenance à la stratégie de maintenance de type âge. Ainsi, pour chacune de ces quatre stratégies de maintenance les valeurs optimales de ses variables de décision et les valeurs optimales correspondantes des performances sont déterminées. Enfin, la stratégie de maintenance de type âge conjuguant avec la disponibilité du personnel de maintenance donnant les meilleures valeurs de ces demières a été retenue comme la meilleure, suivie de la stratégie de maintenance de type âge en deuxième place, de celle de type bloc étendu en troisième place et enfin de stratégie de maintenance de type bloc en demier

Manutenção de equipamentos com múltiplos componentes :: um modelo de programação dinâmica /

Dissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico

Optimizing Implanted Cardiac Device Follow-Up Care

Cardiovascular implantable electronic devices (CIEDs) are life-saving devices programmed to detect cardiac arrhythmias and intervene with pacing or shocks to avoid cardiac death. Currently, three to four million Americans rely on CIEDs and this number is growing rapidly with approximately 400,000 new device implantations each year. Worldwide, around one million new device implantations are performed annually. CIEDs consist of battery-powered pulse generators connected to the heart by one or more electrode wires, called "leads," embedded within a patient's vein. To achieve the maximum possible clinical benefit, modern CIEDs can automatically transmit data to the clinician's office through various media, such as email and text messaging, to allow for remote monitoring. This dissertation concentrates on improving the quality of care of patients with CIEDs, i.e., maximizing the expected lifetime of these patients, by focusing on three major challenges inherent to these devices: (i) cardiac leads fail stochastically and it is not clear whether to abandon them or to extract them, either immediately or at a later time; (ii) the average life span of CIED batteries is not as long as the average patient's expected lifetime and it is not clear when to replace the battery-powered pulse generators; (iii) the remote monitoring of CIEDs can adversely affect the battery's remaining lifetime and it is not clear how frequently the remote transmissions should be performed. We use methodologies including Markov decision processes as well as applied probability and statistics to formulate and analyze decision models that enable clinicians to provide patients with better quality of care. Using clinical data and expert opinion, we carefully calibrate the models concerning challenges (i) and (ii); for (iii), we provide insightful numerical examples for a stylized model. Our results suggest that behaving optimally can significantly extend patients' lives while simultaneously decreasing the burden on the healthcare system by reducing the number of surgeries, in-office visits, and so on, without compromising the patients' well-being

Post-Sale Cost Modeling and Optimization Linking Warranty and Preventive Maintenance

Ph.DDOCTOR OF PHILOSOPH