New methodology for improving the inspection policies for degradation model selection according to prognostic measures

Thi Phuong Khanh Nguyen (1) , Mitra Fouladirad (2) , Antoine Grall (2)

1 LGP - Laboratoire Génie de Production
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

Health monitoring data are vital for failure prognostic and maintenance planning. 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 inpractible 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 operation and maintenance costs but also ensure the information quality. We investigate different types of inspection policies including periodic or non-periodic 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 Wiener and Gamma processes that have been largely addressed in the framework of degradation modeling.

Mots clés: Model selection; Degradation process; Prognostic prediction; RUL prediction; Non-periodic inspection

Domaines: Autre

Thi Phuong Khanh Nguyen, Mitra Fouladirad, Antoine Grall. New methodology for improving the inspection policies for degradation model selection according to prognostic measures. IEEE Transactions on Reliability, 2018, 67 (3), pp.1269-1280. ⟨10.1109/TR.2018.2829738⟩. ⟨hal-02114163⟩