Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches

Industrial assets exposed to random environment often exhibit complex deterioration mechanisms with health status variations. In actual field operation, hidden defect signals are usually crucial indicators of upcoming malfunctions and also reminders of proactive maintenance executions. Despite the extensive applications of defect-centered maintenance, in the literature, little attempt has: a) captured the impact of random environments on health variation and restoration, and b) explored the differentiated functions of maintenance windows in separate states. This article addresses these challenges by introducing a state-based maintenance policy with multifunctional maintenance windows. The impact of environmental disturbance on both defect initialization and propagation is characterized by random increment of the state transition rate as well as probabilistic malfunction risk. Three types of maintenance windows (regular, opportunistic, and postponed) are scheduled to ensure a flexible scheduling of inspection and spare part resources. Importantly, the function of opportunistic window is state-based, defect identification when normal and removal when defective. The objective is to minimize the cost rate via the joint optimization of inspection interval, postponed interval, and opportunistic threshold. Experimental studies demonstrate the superior performance of this policy over some conventional policies.

State-Based Opportunistic Maintenance With Multifunctional Maintenance Windows

Optimum opportunistic maintenance schedule incorporating delay time theory with imperfect maintenance

Postponed maintenance scheduling integrating state variation and environmental impact

Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines

A PSO approach for the integrated maintenance model

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A PSO approach for the integrated maintenance model