Uncertain process–based reliability and maintenance modeling for systems under mutually dependent degradation and shock processes

Abstract

For the systems that experience competing failure processes, an uncertain process–based degradation model is developed to describe the systems. The competing degradation process is composed of internal continuous degradation and external shocks, and the mutual dependence between them is considered. When the magnitude of the internal degradation exceeds the threshold, the soft failure occurs. While for the shock processes involving the randomness and the subjective information, we adopt the uncertain random renewal reward process to characterize it. Hard failure occurs when the damage of the shock process exceeds the strength threshold of the system. By using the belief reliability metric, the reliability of the degraded system is defined as the chance measure that neither soft failure nor hard failure occurs. And the effect of the degradation‐shock dependence on the system reliability is performed by the parametric studies. Then the proposed degradation model is introduced into the preventive maintenance strategy to minimize the average maintenance cost. Using the microelectromechanical systems as an example, the effectiveness of the constructed degradation model and maintenance strategy is illustrated, and the proposed model can characterize the system degradation process in a superior way to the stochastic process model. These methods can be applied to other similar degraded systems and provide support for maintenance decisions.

Optimal maintenance period and maintenance sequence planning under imperfect maintenance

“…In practice, downtimes of equipment and systems are costly 15,16 . Preventive maintenance can prevent equipment from serious failures with long downtimes 17–19 . For achieving the goal above, Zhang and Gao 20 proposed a maintenance policy that sets a limit to the cumulative failure rate over the life cycle of an infrastructure system.…”

“…15,16 Preventive maintenance can prevent equipment from serious failures with long downtimes. [17][18][19] For achieving the goal above, Zhang and Gao 20 proposed a maintenance policy that sets a limit to the cumulative failure rate over the life cycle of an infrastructure system. Jung and Park 21 developed the optimal periodic preventive maintenance policies following the expiration of the warranty.…”

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

“…Besides, the reliability of systems considering random shocks has been extensively investigated. In these studies, Poisson process 23–26 is the most fundamental independent incremental process and is suitable for characterizing general shock processes. Depending on the failure mode, the shocks models are mainly separated into six categories: cumulative shock model, run shock model, extreme shock model, m ‐shock model, δ ‐shock model, and mixed shock model 27–31 …”

Modified nonlinear cumulative damage model considering residual strength degradation and gear reliability analysis

“…[20][21] Zhang et al established a degradation model based on uncertain degradation processes to describe systems with competitive fault processes. 22 Wang and Robert in order to deal with the nonlinear and non-uniform problems existing in the degradation of performance, expressed performance degradation as the accumulation of continuous increment and jump increment, and obtained an explicit expression of probability distribution for predicting degradation and remaining service life. 23 Huang et al proposed a new fusion prediction method for residual useful life (RUL) prediction based on deep learning, which combined the advantages of bidirectional long short-term memory network and particle filter.…”