An integrated systemic model for optimization of condition-based maintenance with human error

This paper proposes an integrated systemic model for the integration of human reliability model with condition based maintenance (CBM) optimization. The problem of CBM optimization is formulated as finding the optimum parameters of a function for condition monitoring (CM) scheduling so that the average unit cost (AUC) of CBM system is minimized. The concept of functional resonance is employed to analyze human-induced failure scenarios emergent from erroneous functional dependencies. To quantify human reliability in CBM, the functional characteristics of human error in CBM as well as the main performance influencing factors (PIFs) are identified. The algorithms of diagnostics and prognostics are integrated in the simulation model of CBM. Then an exact simulation-optimization algorithm based on the use of two joint Fibonacci algorithms is proposed for global optimization of CM scheduling with human error. A sensitivity analysis has been performed based on the newly developed model considering multiple levels of human errors in CBM functions to observe the effects of human errors on overall system cost. The model is also useful in demonstrating the importance and effects of improving human and organizational aspects as well as technical aspects such as the accuracy and relevance of CM technology and the accuracy of prognostics algorithm.