Joint optimization of monitoring quality and replacement decisions in condition-based maintenance

Nguyen, Khanh T. P.; Van, Phuc Do; Huynh, Khac Tuan; Bérenguer, Christophe; Grall, Antoine

doi:10.1016/j.ress.2019.04.034

Cited by 56 publications

(21 citation statements)

References 26 publications

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“…Nguyen et al 22 consider the problem of adjusting condition monitoring quality characterized by the observation noises on the system degradation level returned by an inspection. The optimization problem is formulated based on a generalization of MDP, the partially observed Markov decision process (POMDP), and the authors conclude that the dynamic policy contemplating adjustment of the quality of inspections outperforms the classical fixed‐quality policies.…”

Section: Markov Decision Process (Mdp)mentioning

confidence: 99%

A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

Costa

Braga

Andrade

2020

Quality & Reliability Eng

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Wheelsets absorb a significant part of the maintenance budget of any train operating company. Although wheel wear has been an extensively discussed topic in the literature, wear rates are very rarely characterized by using degradation data in a real‐world case study aimed at identifying optimal maintenance policies including both degradation and recovery modeling. Furthermore, wheel defects, which impose an additional challenge to the modeling of the lifecycle of the wheels, are usually considered separately in the literature. In this study, conducted at a Portuguese train operating company, 17 years of inspection data are used to estimate wheel wear rates and survival curves, which are further incorporated into a Markov decision process (MDP) model. A bidimensional framework considering discrete intervals of wheel diameter along with a quantitative variable (kilometers since last turning/renewal) is used to represent the possible wheel states, while the probability of a defect interfering with the wheel maintenance schedule is modeled by contemplating survival curves derived from a Cox proportional‐hazards model. Optimal results in terms of minimal cost policy are discussed in the context of the MDP, but a more realistic and easy‐to‐implement policy fixing one of the parameters is compared with the optimal policy. Results showed that in practice train operating companies might benefit from using the easy‐to‐implement policy, which has an associated long‐run average cost only about 1% higher than the one suggested by the optimal decision map.

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Section: Markov Decision Process (Mdp)mentioning

confidence: 99%

A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

Costa

Braga

Andrade

2020

Quality & Reliability Eng

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“…Lopes 30 proposed a maintenance model including quality control, preventive maintenance, and buffer decisions. Nguyen et al 31 investigated a maintenance model by monitoring quality and replacement decisions in a condition-based maintenance.…”

Section: Introductionmentioning

confidence: 99%

A Combined Maintenance Strategy Considering Spares, Buffer, and Quality

Gan

Wang

Song

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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This paper studies an innovative maintenance model for an upstream machine in a common production system. Multiple system-wise features are integrated in the model, including maintenance, spare parts, buffer inventory, and product quality. The objective is to minimize the system expected cost rate by determining the optimal inspection interval, preventive maintenance threshold, corrective maintenance threshold, reorder level of spare parts, maximum stock level of spare parts, and maximum inventory level of the buffer. In this maintenance model, both the hazard rate and the product qualification rate can trigger maintenance activities. The well-known proportional hazard model (PHM) is adopted to describe the state of the upstream machine. The effect of machine state on product quality is explicitly characterized. Due to the complexity of the mathematical model, simulation and a genetic algorithm are employed to determine the optimal solution. A case study of bearings is presented to demonstrate the performance of the proposed maintenance policy. The results show that this policy is practical and can reduce system cost significantly.

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“…The lowest expected cost per unit time was produced with superior reliability. Moreover, a dynamic CBM model based on partially observable Markov decision processes was developed to investigate the effect of adjusting condition monitoring quality on the total cost for a stochastic continuous degradation process of a single-unit system [22]. Another example of the joint CBM model considering the production of nonconforming items was proposed for both serial production systems and serial-parallel multistage production systems to reduce the total cost and improve the system output at the same time [23] [24].…”

Section: Introductionmentioning

confidence: 99%

Optimal Condition-Based Maintenance Strategy via an Availability-Cost Hybrid Factor for a Single-Unit System During a Two-Stage Failure Process

2021

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This paper develops an optimal condition-based maintenance (CBM) strategy for a singleunit system during two-stage failure, a process that includes a normal stage and delay-time stage. Both stages are divided into the in-control state and out-of-control state. In the in-control state, the items are always produced with 100% quality, whereas in the out-of-control state, the item quality deteriorates, and minor repairs are arranged accordingly to fix this problem. This causes the optimal CBM strategy for system production to exhibit four different scenarios, where different calculations are carried out using renewal reward theory to determine the system profit. Then, the system profit is optimized by an availability-cost hybrid factor that balances the "cost per unit time" and "availability". Finally, to investigate the effects of different decision objectives on the optimization results, a sensitivity study of the cost parameter and availability-cost weight factor is conducted on the optimized results through numerical simulations. According to the simulation results, this availability-cost hybrid factor, as well as the "cost per unit time" and "availability" factors, become less sensitive when it exceeds 0.6. INDEX TERMS Condition-based maintenance, delay time, hybrid evolution factor, inspection, two-stage failure process.

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Joint optimization of monitoring quality and replacement decisions in condition-based maintenance

Abstract: OATAO is an open access repository that collects the work of some Toulouse researchers and makes it freely available over the web where possible.

Cited by 56 publications

References 26 publications

A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

A Combined Maintenance Strategy Considering Spares, Buffer, and Quality

Optimal Condition-Based Maintenance Strategy via an Availability-Cost Hybrid Factor for a Single-Unit System During a Two-Stage Failure Process

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