A new model for the optimization of periodic inspection intervals with failure interaction: A case study for a turbine rotor

Reliability evaluation plays a critical role in upgrading the availability and productivity of automotive manufacturing industries by adopting the well-planned maintenance. Due to the lack of operation management studies in automotive industry, this paper addresses an operational reliability evaluation through failure behavior trend in an automotive production line. The main approaches for reliability analysis in this study include statistical structure and Monte Carlo simulation model. The statistical structure consists of three steps: data acquisition and homogenization process, validity of the trend hypothesis and parameters estimation. The reliability evaluation under statistical approach identified the main bottlenecks through the recognized behavior trend of system so that needs to be considered as a priority. Besides, K-R algorithm as Monte Carlo simulation was carried out to simulate reliability regarding failure distribution function. The result of Monte Carlo simulation with different iterations provides a high prediction accuracy of reliability with the lowest error. In addition, regarding the computed reliability through the proposed approaches and total expected cost, a reliability-based maintenance optimization model was conducted. The proposed maintenance intervals could be useful for improving the operational performance of critical components in automotive system.

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“…According to the work of Rezaei (2015), the optimal maintenance interval can be obtained as follows:…”

Section: Maintenance Optimization Modelmentioning

confidence: 99%

Operational reliability evaluation-based maintenance planning for automotive production line

Soltanali

Rohani

Tabasizadeh

et al. 2019

Quality Technology & Quantitative Management

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“…In preference to previous works (e.g. see [8,9,11,13,17,23,[25][26][27][28][29][31][32][33]), both the cost structure and the inspection model explored here respond to the variation of the system state. Examples of papers that use particular cost structures can be found in [11,23,26,33].…”

Section: Related Workmentioning

confidence: 99%

“…Maintenance optimization models based on the periodic inspection policy is vast (e.g. see [8,9,17,18,25,28,29]). For example, Lienhardt [17] study both the corrective maintenance and a periodic inspection policy for a repairable system subject to non-self announcing failures.…”

Section: Related Workmentioning

confidence: 99%

A novel data-driven approach to optimizing replacement policy

Ahmadi

2017

Reliability Engineering & System Safety

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Parallel systems are a commonly used structure in reliability engineering. A common characteristic of such systems is that the failure of a component may not cause its system to fail. As such, the failure may not be immediately detected and the random (disruption) time at which the number of failed components reaches a certain predefined number d may therefore be unknown. For such systems, scheduling maintenance policy is a difficult task, which is tackled in this paper. The paper assumes that times between inspections conform to a modulated Poisson process. This assumption allows the frequency of inspection responds to the variation of the disruption state. The paper then estimates the disruption time on the basis of inspection point process observations in the framework of filtering theorem. The paper develops a unified cost structure to jointly optimise inspection frequency and replacement time for the system when the lifetime distribution of a component follows the Pareto or exponential distribution. Numerical results are provided to show the application of the proposed model. Nomenclature mNumber of components in the system t r Periodic replacement time X d (t) Disruption state τ d Disruption (alert) time at which the process X d (t) describing the system state jumps from normal state (0) to the degraded state (1)

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“…Niu & Jiang [17] presented a maintenance optimization strategy with health-oriented prognostic control and global optimization considered together. Rezaei [18] assumed that the failure of hard-fault components would accelerate that of soft-fault ones, and then established a periodic inspection optimization model considering stochastic dependence. Bouvard et al [19]…”

Section: Introductionmentioning

confidence: 99%

Maintenance Optimization for Two-Component Series Systems With Degradation Dependence

Yang

et al. 2021

IEEE Access

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This paper proposes a model of maintenance optimization for a system consisting of two dependent components in long-term operation, where both components are subject to dependent degradation. Gamma process is used to model the degradation, and Frank Copula is applied to describe the degradation dependence between the two components. We then consider degradation dependence to optimize maintenance strategy for the system to maximize its availability. The improved Artificial Bee Colony (ABC) algorithm is used to jointly determine inspection interval, opportunistic and preventive maintenance thresholds of the system. Finally, an example is provided to validate the proposed model.

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A new model for the optimization of periodic inspection intervals with failure interaction: A case study for a turbine rotor

Cited by 28 publications

References 26 publications

Operational reliability evaluation-based maintenance planning for automotive production line

Operational reliability evaluation-based maintenance planning for automotive production line

A novel data-driven approach to optimizing replacement policy

Maintenance Optimization for Two-Component Series Systems With Degradation Dependence

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