An Adaptive Maintenance Policy With Nonlinear Degradation Modeling Based on Prognostic Information

Abstract

Prognostics and health management (PHM) technology is an extremely important research focus in the field of reliability engineering. The ultimate goal of applying PHM technology is health management. Aiming at nonlinear degradation systems, an adaptive maintenance policy based on prognostic information is proposed herein. First, a nonlinear degradation model with an adaptive updating mechanism is used to predict the remaining useful life (RUL) of the degrading system. Then, based on the predicted RUL distribution, a multi-objective optimization model is established to address the trade-off between operating cost and availability through a constructed decision boundary, instead of the approach used in previous studies, which considers cost as a single indicator. Using this multi-objective optimization model, an adaptive decision criterion is proposed to evaluate the advantages and disadvantages of different replacement policies, in order to determine the optimal replacement time and dynamic condition monitoring (CM) interval of the degrading system. Finally, an example of gyroscope in an inertial navigation system (INS) is used to verify the effectiveness of the proposed method.

Keywords

• Health management
• prognostic information
• multi-objective optimization
• replacement