Review of Markov models for maintenance optimization in the context of offshore wind

The offshore environment poses a number of challenges to wind farm operators. Harsher climatic conditions typically result in lower reliability while challenges in accessibility make maintenance difficult. One of the ways to improve availability is to optimize the Operation and Maintenance (O&M) actions such as scheduled, corrective and proactive maintenance. Many authors have attempted to model or optimize O&M through the use of Markov models. Two examples of Markov models, Hidden Markov Models (HMMs) and Partially Observable Markov Decision Processes (POMDPs) are investigated in this paper. In general, Markov models are a powerful statistical tool, which has been successfully applied for component diagnostics, prognostics and maintenance optimization across a range of industries. This paper discusses the suitability of these models to the offshore wind industry. Existing models which have been created for the wind industry are critically reviewed and discussed. As there is little evidence of widespread application of these models, this paper aims to highlight the key factors required for successful application of Markov models to practical problems. From this, the paper identifies the necessary theoretical and practical gaps that must be resolved in order to gain broad acceptance of Markov models to support O&M decision making in the offshore wind industry

Optimal Replacement of Continuously Degrading Systems in Partially Observed Environments

Abstract Motivated by wind energy applications, we consider the problem of optimally replacing a stochastically degrading component that resides and operates in a partially observable environment. The component's rate of degradation is modulated by the stochastic environment process, and the component fails when its accumulated degradation first reaches a fixed threshold. Assuming periodic inspection of the component, the objective is to minimize the long-run average cost per unit time of performing preventive and reactive replacements for two distinct cases. The first case examines instantaneous replacements and fixed costs, while the second considers time-consuming replacements and revenue losses accrued during periods of unavailability. Formulated and solved are mixed state space, partially observable Markov decision process (POMDP) models, both of which reveal the optimality of environment-dependent threshold policies with respect to the component's cumulative degradation level. Additionally, it is shown that for each degradation value, a threshold policy with respect to the environment belief state is optimal if the environment alternates between two states. The threshold policies are illustrated by way of numerical examples using both synthetic and real wind turbine data

Optimal replacement in the proportional hazards model and its applications in a product-service system

Condition-based maintenance is rapidly gaining favor as a way to prevent the failures of capital-intensive assets and to maintain them in good operating condition with minimum cost. A valuable and increasingly prevalent way to incorporate condition information into risk estimation is by the proportional hazards model (PHM), which explicitly includes both the age and the condition information in the calculation of the hazard function. This dissertation consists of three papers, in which the optimal replacement policies for systems whose deterioration process follows the PHM are developed under different settings; and a joint optimization of the asset and inventory management problem in the context of a product-service system is considered. In the first paper, a continuous time Markov covariate process is assumed to describe the condition of a system that is under periodic monitoring. Although the form of an optimal replacement policy for such a system in the PHM was developed previously, an approximation of the Markov process as constant within inspection intervals led to a counter-intuitive result that less frequent monitoring could yield a replacement policy with lower average cost. Accounting for possible state transitions between inspection epochs removes the approximation and eliminates the cost anomaly. A new recursive procedure to obtain the parameters of the optimal replacement policy is presented. By comparing the replacement and monitoring costs of different monitoring scheme, the value of condition information is evaluated. In the second paper, the optimal replacement policy for systems in the PHM with semi-Markovian covariate process and continuous monitoring is developed. Numerical examples and sensitivity analysis provide some insights about the suitability of a Markov approximation and the impact of the variations in the input parameters on the cost. In applying the optimal replacement policies to a product-service system, where the producers provide the use of the products to customers while retaining ownership, the coupling between the decision making for preventive replacement and the decision making for inventory management is evident. In the third paper, an integrated model is proposed for the preventive maintenance of a fleet of products and the inventory management of a hybrid manufacturing-remanufacturing system in the context of a product-service system. A joint optimization technique is developed to obtain the optimal parameters for the operational policy of the integrated model to minimize the long run average cost per unit time. In addition, the effect of the assumption that the replaced products are not sorted is evaluated

Traitement des valeurs manquantes pour l'application de l'analyse logique des données à la maintenance conditionnelle

RÉSUMÉ La qualité des données d’apprentissage est une problématique dans de nombreuses applications de classification supervisée, car la qualité de classification de n’importe qu’elle méthode est définie par la qualité des données utilisées dans le processus de traitement c.à.d. à l’entrée du système de classification. Lors de l’utilisation du classificateur basé sur la méthode d’analyse logique des données (LAD) dans le domaine de la maintenance conditionnelle, il est très fréquent de confronter le problème de données manquantes. Ce phénomène se manifeste lorsque les valeurs n’ont pas pu être observées, elles ont été perdues ou elles n’ont pas été enregistrées. La présence de ces dernières entraîne un dysfonctionnement du processus de traitement logique des données, puisque le classificateur LAD, ne peut pas apprendre à partir des bases de données incomplètes. Si l’on veut l’utiliser, il faut donc adopter une méthode d’imputation de ces données. En l’absence d’une méthode de traitement des données numériques manquantes pour le classificateur LAD, l’élaboration d’une nouvelle méthode statistique s’avère une alternative très intéressante pour substituer les données manquantes et, par la suite, générer des modèles de classification par LAD. Dans cette optique, nous proposons dans ce mémoire une méthode statistique de substitution des valeurs manquantes. L’objectif de cette méthode est de remplacer la valeur manquante par les deux possibilités extrêmes que peut prendre cette valeur suivant les valeurs disponibles de la variable en question, et suivant l’information des classes dont on dispose. Nous avons également mis l’accent sur la validation de notre approche, qui a bénéficié des techniques du test statistique non paramétrique. Cela nous a permis de confirmer les résultats de différents tests de la nouvelle méthode sur des données réelles dans le cadre de trois applications concernant la classification supervisée.----------ABSTRACT The quality of learning data is an issue in a number of applications of monitored classification, as the classifying quality in any method is defined by the quality of the data used in the processing phase; i.e. at the entry of classification system. It is very common to face the issue of missing data when using the classifier based on the logical analysis of data method (LAD). This phenomenon is noticed when values can not be noted, are lost or have not been saved. One of these cases causes a dysfunction of the logical processing of data, as the classifier LAD cannot get its information from incomplete databases. Should we use it, we should adopt a method that removes this data. In the absence of a method of processing missing digital data for classifier LAD, setting up a new statistical method would appear as a very beneficial alternative to catch up for missing data and then to create classification patterns using LAD. In this perspective, we propose in this work a statistical method to substitute missing values. The aim of this thesis is to search how to replace the missing value with the two extreme possibilities following the available values of the variable in question, and following the information on the classes available. We also focus on the validation of our approach that took advantage from techniques of the non-parametrical statistical test. This allowed us to reassert the results of the various tests of the new method on true data as per three applications concerning the monitored classification. The works presented in this thesis are the outcome of the ambitious research project led by the team of Dr. Soumaya Yacout. They are also the continuation of the works included in the thesis of David S. (David S. 2007) focused on the introduction of the logical analysis of data for conditional maintenance

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

Ph.DDOCTOR OF PHILOSOPH