Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process

Abstract

To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction.