伽马降解工艺的维护不完善，比旧工艺更糟糕

IF 1.3 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2024-02-16 DOI:10.1002/asmb.2849

Franck Corset, Mitra Fouladirad, Christian Paroissin

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引用次数: 0

摘要

这篇文章考虑的是对存在劣化问题的系统进行基于状态的维护。劣化是以非均质伽马过程为模型的，更确切地说，伽马过程和预防性维护都是不完善的，或者说是不如旧的。纠正性维护行动与新的一样好。维护效率或非效率参数以及劣化参数被认为是未知的。所考虑的监测数据提供了有关维护参数的间接信息。因此，采用期望最大值算法进行参数估计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Imperfect and worse than old maintenances for a gamma degradation process

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Imperfect and worse than old maintenances for a gamma degradation process

This article considers a condition-based maintenance for a system subject to deterioration. The deterioration is modeled by a non-homogeneous gamma process, more precisely the gamma process and the preventive maintenance are imperfect or worse than old. The corrective maintenance actions are as good as new. The maintenance efficiency or non-efficiency parameters as well as the deterioration parameters are considered to be unknown. The monitoring data under consideration give indirect information on the maintenance parameters. Therefore, an expected maximum algorithm is applied for parameter estimation.

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来源期刊

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.