A parametric predictive maintenance decision framework considering the system health prognosis accuracy
Proceedings of Applied Mathematics in Engineering and Reliability: Proceedings of the 1st International Conference on Applied Mathematics in Engineering and Reliability
Nowadays, the health prognosis is popularly recognized as a significant lever to improve the maintenance performance of modern industrial systems. Nevertheless, how to efficiently exploit prognostic information for maintenance decision-making support is still a very open and challenging question. In this paper, we attempt at contributing to the answer by developing a new parametric predictive maintenance decision framework considering improving health prognosis accuracy. The study is based on a single-unit deteriorating system subject to a stochastic degradation process, and to maintenance actions such as inspection and replacement. Within the new framework, the system health prognosis accuracy is used as a condition index to decide whether or not carrying out an intervention on the system. The associated mathematical cost model is also developed and optimized on the basis of the semi-regenerative theory, and is compared to a more classical benchmark framework. Numerical experiments emphasize the performance of the proposed framework, and confirm the interest of introducing the system health prognosis accuracy in maintenance decision-making.