Condition-based maintenance for complex systems based on current component status and Bayesian updating of component reliability

Walter, GM Gero; Flapper, Sdp Simme Douwe

doi:10.1016/j.ress.2017.06.015

Cited by 41 publications

(16 citation statements)

References 16 publications

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“…An expert-judgement process for fusing multi-source prior information was developed by Yang et al [30]. Various available sources of expert knowledge and data, at both subsystem and system levels, were integrated [31], and methods for fusing lifetime data and expert information were discussed [32].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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Residual life estimation is an important problem in reliability engineering. Traditional methods, which are based on time-to-failure distribution, have limitations for components of on-orbit satellites characterized as high reliability with small sample size. Various types of reliability information can be collected during test and operation, including historical lifetime data, degradation data, similar data, expert information, etc. Therefore, making full use of multi-source information is meaningful for improving estimation precision. However, research on residual life estimation by fusing multi-source information is rare. No study has examined the overall process of fusing all of the different kinds of information. In this paper, a Bayesian method is presented to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing all the collected information. Prior distributions are determined using different kinds of information. After fusing the field data, posterior distributions can be obtained corresponding to each prior distribution. Then, the joint posterior distribution is the weighted sum of these posterior distributions with weights calculated using the second Maximum Likelihood Estimation (ML-II) method. Consistency is tested to guarantee the safety of the information fusion. Furthermore, residual life is estimated by the proposed sample-based method including both the Bayesian estimate and credible interval (CI). A Monte Carlo simulation study is conducted to demonstrate the proposed methods and shows that the Bayesian method is satisfactory and robust. Finally, a published dataset of the momentum wheel in a satellite is analyzed to illustrate the application of the method.

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Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

2019

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“…Patelli et al [24] developed a survival signature-based simulation method to calculate the reliability of large and complex systems and [25] presents a simulation method which can be used if the dependency structure is too complex for a survival signature approach. Walter et al [26] proposed a new conditionbased maintenance policy for complex systems using the survival signature. Moreover, Eryilmaz et al [27] generalized the survival signature to multistate systems.…”

Section: Phased Mission Systemsmentioning

confidence: 99%

Reliability analysis of general phased mission systems with a new survival signature

Huang

Aslett

Coolen

2019

Reliability Engineering & System Safety

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It is often difficult for a phased mission system (PMS) to be highly reliable, because this entails achieving high reliability in every phase of operation. Consequently, reliability analysis of such systems is of critical importance. However, efficient and interpretable analysis of PMSs enabling general component lifetime distributions, arbitrary structures, and the possibility that components skip phases has been an open problem.In this paper, we show that the survival signature can be used for reliability analysis of PMSs with similar types of component in each phase, providing an alternative to the existing limited approaches in the literature. We then develop new methodology addressing the full range of challenges above. The new method retains the attractive survival signature property of separating the system structure from the component lifetime distributions, simplifying computation, insight into, and inference for system reliability.

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“…More and more online monitoring technology is used to monitor machinery with high reliability demands [1]. Before mechanical failure, in order to maintain higher reliability and lower operating costs, the machine is maintained at the appropriate time by using actual situation information [2]. This maintenance strategy is called Condition-Based Maintenance (CBM) or Prognostic and Health Management (PHM).…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network

Yang¹,

Guo²,

Dou³

2019

IJPE

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The remaining useful life is key to the decision-making of machinery maintenance. The online prediction of remaining useful life has become a very urgent need for mechanical equipment with high reliability requirements. The aim of this paper is to provide a simple and effective method for predicting the remaining life of the machine under the condition of small sample. The Kolmogorov-Smirnov test theory is used to extract the health state feature of the machine. Based on the Long and Short Term Memory (LSTM) theory, an online method of remaining useful life prediction is proposed. The bearing life vibration data verification shows that the Kolmogorov-Smirnov distance is sensitive to the development and expansion of the defects. Furthermore, the proposed method of remaining useful life prediction based on LSTM theory has high prediction accuracy. The technician can then use this method to take appropriate maintenance operations.

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Condition-based maintenance for complex systems based on current component status and Bayesian updating of component reliability

Cited by 41 publications

References 16 publications

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Reliability analysis of general phased mission systems with a new survival signature

Remaining Useful Life Prediction of Machinery based on K-S Distance and LSTM Neural Network

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