Remaining useful life prediction of mechanical system based on performance evaluation and geometric fractional Lévy stable motion with adaptive nonlinear drift

Abstract

Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM). To accurately predict the RUL of mechanical system under complex conditions, an RUL prediction framework is proposed based on performance evaluation and geometric fractional Lévy stable motion (GFLSM) with adaptive nonlinear drift. The early fault identification of degradation process is realized by setting a threshold for the constructed monotonic health indicator (HI). The dynamic updating method of failure threshold depending on confidence interval is proposed to determine the time of zero RUL. The heavy-tailed distribution degradation model based on GFLSM is constructed to overcome the limitation of Gaussian distribution. The multiple degradation stages are mapped to a relatively unified mode through GFLSM. The long-range dependence and self-similarity of degradation process are described through the relationship between Hurst exponent and stability exponent. The adaptive updating method of nonlinear drift coefficient is put forward to satisfy different degradation trajectories, and other parameters of GFLSM are estimated by the characteristic function method. The predicted RUL and corresponding probability density function (PDF) are obtained by Monte Carlo. The proposed RUL prediction framework is verified by the degradation simulation signal and two different practical industrial experiments. The experimental results demonstrate that the proposed framework is more effective and superior to other state-of-the-art techniques in RUL prediction of mechanical system.