Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference

Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference

Risk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the...

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Main Authors: Tianhua Xu, Tao Tang, Haifeng Wang, Tangming Yuan
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/947104
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spelling doaj-12a27ed390e24622bb099017eae237e62020-11-25T00:01:22ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/947104947104Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic InferenceTianhua Xu0Tao Tang1Haifeng Wang2Tangming Yuan3State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaNational Engineering Research Centre of Rail Transportation Operation and Control Systems, Beijing Jiaotong University, Beijing 100044, ChinaComputer Science Department, University of York, York YO10 5GH, UKRisk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.http://dx.doi.org/10.1155/2013/947104
collection DOAJ
language English
format Article
sources DOAJ
author Tianhua Xu
Tao Tang
Haifeng Wang
Tangming Yuan
spellingShingle Tianhua Xu
Tao Tang
Haifeng Wang
Tangming Yuan
Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
Mathematical Problems in Engineering
author_facet Tianhua Xu
Tao Tang
Haifeng Wang
Tangming Yuan
author_sort Tianhua Xu
title Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
title_short Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
title_full Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
title_fullStr Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
title_full_unstemmed Risk-Based Predictive Maintenance for Safety-Critical Systems by Using Probabilistic Inference
title_sort risk-based predictive maintenance for safety-critical systems by using probabilistic inference
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description Risk-based maintenance (RBM) aims to improve maintenance planning and decision making by reducing the probability and consequences of failure of equipment. A new predictive maintenance strategy that integrates dynamic evolution model and risk assessment is proposed which can be used to calculate the optimal maintenance time with minimal cost and safety constraints. The dynamic evolution model provides qualified risks by using probabilistic inference with bucket elimination and gives the prospective degradation trend of a complex system. Based on the degradation trend, an optimal maintenance time can be determined by minimizing the expected maintenance cost per time unit. The effectiveness of the proposed method is validated and demonstrated by a collision accident of high-speed trains with obstacles in the presence of safety and cost constrains.
url http://dx.doi.org/10.1155/2013/947104
work_keys_str_mv AT tianhuaxu riskbasedpredictivemaintenanceforsafetycriticalsystemsbyusingprobabilisticinference
AT taotang riskbasedpredictivemaintenanceforsafetycriticalsystemsbyusingprobabilisticinference
AT haifengwang riskbasedpredictivemaintenanceforsafetycriticalsystemsbyusingprobabilisticinference
AT tangmingyuan riskbasedpredictivemaintenanceforsafetycriticalsystemsbyusingprobabilisticinference
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