New Methodology for Improving the Inspection Policies for Degradation Model Selection According to Prognostic Measures

Health monitoring data are vital for failure prognostic and maintenance planning. The continuous monitoring data or frequent inspections can provide a large amount of information on degradation evolution and therefore ensure the quality of deterioration modeling and the lifetime prognostic accuracy. However, they are usually very costly and sometimes impracticable in real engineering applications. Therefore, it is essential to address the issue of the appropriate amount of monitoring data. This paper proposes a new methodology to help the companies improving their actual inspection/monitoring policy to reduce the operation and maintenance costs but also ensure the information quality. We investigate different types of inspection policies including the periodic or nonperiodic ones by considering multiples functions of the system degradation state that are linear, concave, or convex. The best policies are chosen based on a multiobjective optimization problem dealing with the inspection cost and the information level. The advantages and disadvantages of the proposed methodology are discussed through numerous numerical examples for different types of degradation process, particularly the Wiener and Gamma processes that have been largely addressed in the framework of degradation modeling.