Modeling dependent competing failure processes with degradation-shock dependence

In this paper, a repairable multi-component system is studied where all the components can be repaired individually within the system. The whole system is inspected at inspection intervals and the failed components are detected and replaced with a new one, while the other components continue functioning. Replacing components individually within the system makes their initial age to be different at each inspection time. Different initial age of all the components have affect on the system reliability and probability of failure and consequently the optimal inspection time, which is for the whole system not individual components. A dynamic maintenance policy is proposed to find the next inspection time based on the initial age of all the components. Two competing failure processes of degradation and a shock process are considered for each component. In our paper, there is a mutual dependency between the degradation process and shock process. Each incoming shock adds additional abrupt damages on the cumulative degradation path of all the components, and the shock arrival process is affected by the system degradation process.A realistic numerical example is presented to illustrate the proposed reliability and maintenance model.

show abstract

“…It is assumed that the inspection cost is $5, the downtime cost is $100, the replacement is $20. Fan et al [38] η Facilitation factor 0.2 0.2…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Fan et al [38] Wij Friction caused by contamination lock 2 (10,5 ) The system reliability and optimal maintenance policy is presented for two different models for a jet pipe servo-valve.…”

Section: Numerical Resultsmentioning

confidence: 99%

Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Yousefi

Coit

Zhu

2020

Computers & Industrial Engineering

View full text Add to dashboard Cite

show abstract

“…where the statistic Π kΔ represents the probability that at sampling epoch kΔ the system is in the warning state, given all available information. According to Bayes' theorem, the posterior probability Π kΔ can be updated recursively by where f (y kΔ | x), x = 0, 1 is the PDF of multivariate normal distribution defined in Equation 11. At the initial moment, Π 0 = 0, the transition probabilities in Equation 18 can be obtained using the instantaneous transition rate matrix in Equation 10 by solving the Kolmogorov backward differential equations…”

Section: Residual Life Estimationmentioning

confidence: 99%

“…In fact, the failure modes of gears include not only soft failure because of performance degradation, such as wear, fatigue crack, pitting and teeth broken, but also hard failure, ie, the catastrophic failure caused by extreme operating conditions or hidden manufacturing defects . Most system failures are not caused by a single failure mode but are the results of the competition between different failure modes . In this paper, we consider two dependent failure modes, namely, degradation failure and catastrophic failure, for the gearbox early fault detection.…”

Section: Introductionmentioning

confidence: 99%

“…1,9 Most system failures are not caused by a single failure mode but are the results of the competition between different failure modes. 6,[10][11][12] In this paper, we consider two dependent failure modes, namely, degradation failure and catastrophic failure, for the gearbox early fault detection. The Marshall-Olkin bivariate exponential distribution (BED) is used to describe the correlation of the two failure modes because of its nondecreasing conditional hazard rate property and wide application in the competing risks models.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Makiš

Zhao

et al. 2018

Quality & Reliability Eng

View full text Add to dashboard Cite

Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.

show abstract

Joint optimization of maintenance and spares ordering policy for a use‐oriented product‐service system with multiple failure modes

Zhang

Zhao

Song

et al. 2021

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

The use-oriented product-service system, such as the bike-sharing system and the electric vehicle charging station, has gained wide application in engineering fields. Use-oriented product-service systems are commonly subject to failures of software and hardware. Failed software can be repaired while failures of hardware require the replacement by spares. An innovative joint optimization model of maintenance and spares ordering policy is proposed for a use-oriented product-service system in this work where different types of spares are considered for hardware failure. System effectiveness is maximized by simultaneously considering the incurred costs, system availability, spares availability and the availability of repair engineers. Then, the related reliability probabilistic indices and costs are derived through the continuous time Markov process. To overcome the computational complexity, a greedy search algorithm is applied to obtain a balanced optimal solution for the number of repair engineers and spares inventory policies of different types of parts. Finally, some numerical instances are given to demonstrate the superiority and effectiveness of the proposed joint optimization model.

show abstract

Modeling dependent competing failure processes with degradation-shock dependence

Cited by 136 publications

References 42 publications

Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Joint optimization of maintenance and spares ordering policy for a use‐oriented product‐service system with multiple failure modes

Contact Info

Product

Resources

About