Long-term prediction of system degradation with similarity analysis of multivariate patterns

Abstract

To forecast the long-term degradation behavior of mechanical systems, a method named Distance Based Sequential Aggregation with Gaussian Mixture Model (DBSA-GMM) that completes predictions with two steps was proposed: first, it calculates the statistical distances (SD-s) of the objective degradation signature pattern to historical precursors, then, it uses the SD-S to generate hypothetical Gaussian estimations of the objective features, and synthesizes these Gaussians to build up Gaussian Mixture Model (GMM) approximations of feature Probability Density Functions (PDF-s) with a newly proposed algorithm called Descending Order Aggregation (DOA). DBSA-GMM was applied in the condition prediction of a fleet of commercial aero-engines and showed advantageous prediction precision over Auto-Regressive Moving Average (ARMA), Back Propagation Artificial Neural Network (BP-ANN), and former similarity based prediction (SBP) methods. Meanwhile, DOA was also validated to be with higher generalization ability with additional tests on outlier samples against Kernel Density Estimation (KDE) method.