Optimal condition‐based maintenance policy for systems with mutually dependent competing failure

Abstract

In this paper, a novel condition‐based maintenance (CBM) policy for systems with mutually dependent competing failure processes (DCFPs) is studied. The cumulative random shocks contribute to the increased rate of degradation, meanwhile, the hard failure threshold is reduced when the amount of degradation reaches a threshold. The degradation process is simulated by a non‐stationary Gamma process and random shocks are modeled as a homogeneous Poisson process. Preventive replacement (PR) and corrective replacement (CR) are accounted for in the developed maintenance strategy. The optimal periodic inspection interval, PR threshold, and the number of cumulative random shocks can be derived by minimizing the average long‐term maintenance cost rate. In the end, a numerical case is applied to demonstrate the availability of the developed models.

Joint optimization of condition‐based maintenance and condition‐based reallocation for a system with multiple degrading components

“…[12][13][14][15] CBM looks for system condition information that indicates a failure is occurring or about to occur and takes some proactive actions to prevent the system from failure or to reduce the damage of a system failure. [16][17][18] De Jonge et al 19 proved the relative benefits of CBM over TBM by comparing the cost difference under various practical factors for a single-component system. Van and Bérenguer 20 presented a CBM policy for a single-component deteriorating production system.…”

“…In order to overcome these deficiencies of TBM and ABM, CBM has become a mainstream maintenance method supported by the rapid development of sensor technology 12–15 . CBM looks for system condition information that indicates a failure is occurring or about to occur and takes some proactive actions to prevent the system from failure or to reduce the damage of a system failure 16–18 . De Jonge et al 19 .…”

Reliability modeling of systems with random failure threshold subjected to cumulative shocks using machine learning method

“…Sheu et al 9 proposed a replacement policy for a two-unit system subjected to cumulative shocks. Qi and Huang 10 proposed a reliability model for systems with dependent competing failure processes and cumulative random shocks, and a conditionbased maintenance policy based on their proposed reliability model. Wang et al 11 introduced a cumulative exposure model for predicting remaining useful lifetime (RUL) of pitot tube based on dynamic operating data.…”

Reliability assessment models for competing failure processes with two types of correlative thresholds

“…suggested that when the total amount of degradation, including continuous degradation and sudden degradation increments due to impacts, exceeded a certain threshold, the hard failure threshold may be lowered, leading to a reduction in product strength. Qi and Huang 18 constructed a reliability assessment model by considering the influence of continuous degradation amount on the hard failure threshold. Chang et al 19 .…”

“…Yousefi et al 17 suggested that when the total amount of degradation, including continuous degradation and sudden degradation increments due to impacts, exceeded a certain threshold, the hard failure threshold may be lowered, leading to a reduction in product strength. Qi and Huang 18 constructed a reliability assessment model by considering the influence of continuous degradation amount on the hard failure threshold. Chang et al 19 proposed the effect of degradation level on the degradation rate and the hard failure threshold, and assumed that the hard failure threshold decreased with increasing degradation level for a certain amount of system degradation.…”