Bayesian estimation of Weibull mixture in heavily censored data setting

International audienceLifetime data collected from a fleet of vehicles or, more broadly, park of systems are generally non-homogeneous and heavily censored. Indeed, system lifetime can be affected by the variability of production conditions and usage conditions. Most of the time, this variability is unobserved, but has to be taken into account for reliability or warranty cost analysis. This research proposes a two-component Weibull mixture model for modelling unobserved heterogeneity in in heavily censored lifetime data collection. Performance of classical estimation methods (maximum of likelihood, EM, full Bayes and MCMC) are significantly reduced due to the high number of parameters and the heavy censoring. Therefore , a Bayesian bootstrap method, called Bayesian Restauration Maximisation, is used. Sampling from the posterior distribution is obtained thanks to an importance sampling technique. Simulation results showed that, even with heavy censoring, BRM is effective both in term of estimates precision and computation times. The prior elicitation, sensibility analysis and comparaisons with EM are discussed. Finally, a real data set is analyzed to illustrate the application of the method

Maintenance cost forecasting for a fleet of vehicles

Generally, when purchasing of a fleet of vehicles, after-sales service includes maintenance, repair and overhaul (MRO) contract : this contract guarantees the fleet availability during several years under usage constraints. MRO contract is an undeniable marketing argument for automobile manufacturers, but it is also an additional cost which must be assessed. Particularly as this contracts last far beyond the equipments useful life. To manage the spare parts warehouse and to to assess the costs of maintenance and repairs, manufacturer must be able to forecast the number of failures for a list of critical equipments, throughout the contract. Extensive researches on spare parts inventory management could be found. But in the case of MRO contract on a fleet of vehicles, this issue is particulary difficult. First of all, guaranteed maximum availability during several years requires information on both equipments reliability and vehicles usages. Furthermore, there were many factors of instability of the spare parts requirement over the years: on the one hand equipments may have various modes of ageing either an equipment may be subjected to several mode of ageing ; on the other hand, vehicle usages may vary significantly according to the mission profile. The purpose of this article is forecast the spare parts requirement for a fleet of vehicles, over several years, using a simulation model based on statistical analysis of equipments inter-occurrences of failure and vehicle usages. By integrating this these two different cost drivers over the years, we provide a flexible tool of decision-making to prepare or manage a MRO contract for a fleet of vehicles

Maintien en conditions opérationnelles d'une flotte de véhicules : estimation du besoin en pièce de rechange

International audienceLors de l'achat d'une flotte de véhicules, le service après-vente comprend en général un contrat d’entretien, de réparation et de révision des véhicules : ce contrat garantit la disponibilité de la flotte pendant plusieurs années sous des contraintes d'usage. Si ce type de contrat est un argument marketing indéniable pour les constructeurs automobiles, c'est aussi un coût supplémentaire qui doit être évalué. D'autant plus que ces contrats durent bien au-delà de la durée de vie utile des équipements. Pour gérer le stock de pièces de rechange, évaluer les coûts de maintenance et de réparations, le gestionnaire du contrat doit être en mesure de prévoir le nombre de défaillances pour une liste d'équipementscritiques tout au long du contrat. Il existe cependant de nombreux facteurs d'instabilité du besoin en pièces de rechange au court du temps : d'une part, les équipements peuvent présenter différents modes de vieillissement, être soumis à plusieurs causes de panne ; d’autre part l’usage desvéhicules peut varier de manière significative selon le profil des missions. L'objet de cet article est alors de prévoir les besoins en pièces de rechange d’une flotte de véhicules sur plusieurs années de contrat. Nous avons utilisé pour cela un modèle de simulation basé sur une analyse statistique des défaillances des équipements et une analyse statistique de l’usage des véhicules. En intégrant ces deux facteurs de coûts au fil des ans, nous fournissons un outil flexible de décision pour préparer ou gérer un contrat MRO pour une flotte de véhicules

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.Ministerio de Ciencia, Innovación y Universidades (MICINN). España RTI2018-094614-B-I00 (SMASHING

A Gaussian process based fleet lifetime predictor model for unmonitored power network assets

This paper proposes the use of Gaussian Process Regression to automatically identify relevant predictor variables in a formulation of a remaining useful life model for unmonitored, low value power network assets. Reclosers are used as a proxy for evaluating the efficacy of this method. Distribution network reclosers are typically high-volume assets without on-line monitoring, leading to an insufficient understanding of which factors drive their failures. The ubiquity of reclosers, and their lack of monitoring, prevents the tracking of their individual remaining life, and, confirms their use in validating the proposed process. As an alternative to monitoring, periodic inspection data is used to evaluate asset risk level, which is then used in a predictive model of remaining useful life. Inspection data is often variable in quality with a number of features missing from records. Accordingly, missing inputs are imputed by the proposed process using samples drawn from an advanced form of joint distribution learned from test records and reduced to its conditional form. This work is validated on operational data provided by a regional distribution network operator, but conceptually is applicable to unmonitored fleets of assets of any power network

Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.Proyecto RTI2018-094614-B-I00 (SMASHING), and the “Programa Estatal de I+D+i Orientada a los Retos de la Sociedad

Accelerated life test method for the doubly truncated Burr type XII distribution

[[abstract]]The Burr type XII (BurrXII) distribution is very flexible for modeling and has earned much attention in the past few decades. In this study, the maximum likelihood estimation method and two Bayesian estimation procedures are investigated based on constant-stress accelerated life test (ALT) samples, which are obtained from the doubly truncated three-parameter BurrXII distribution. Because computational difficulty occurs for maximum likelihood estimation method, two Bayesian procedures are suggested to estimate model parameters and lifetime quantiles under the normal use condition. A Markov Chain Monte Carlo approach using the Metropolis–Hastings algorithm via Gibbs sampling is built to obtain Bayes estimators of the model parameters and to construct credible intervals. The proposed Bayesian estimation procedures are simple for practical use, and the obtained Bayes estimates are reliable for evaluating the reliability of lifetime products based on ALT samples. Monte Carlo simulations were conducted to evaluate the performance of these two Bayesian estimation procedures. Simulation results show that the second Bayesian estimation procedure outperforms the first Bayesian estimation procedure in terms of bias and mean squared error when users do not have sufficient knowledge to set up hyperparameters in the prior distributions. Finally, a numerical example about oil-well pumps is used for illustration.[[notice]]補正完

JIDOKA. Integration of Human and AI within Industry 4.0 Cyber Physical Manufacturing Systems

This book is about JIDOKA, a Japanese management technique coined by Toyota that consists of imbuing machines with human intelligence. The purpose of this compilation of research articles is to show industrial leaders innovative cases of digitization of value creation processes that have allowed them to improve their performance in a sustainable way. This book shows several applications of JIDOKA in the quest towards an integration of human and AI within Industry 4.0 Cyber Physical Manufacturing Systems. From the use of artificial intelligence to advanced mathematical models or quantum computing, all paths are valid to advance in the process of human–machine integration