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

Shisheng Zhong, Zhixue Tan and Lin Lin

Reliability Engineering and System Safety, 2019, vol. 184, issue C, 101-109

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.

Keywords: Long-term forecasting; Noisy time series distance measurement; Gaussian Mixture Model; Generalization ability (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:184:y:2019:i:c:p:101-109

DOI: 10.1016/j.ress.2017.11.001

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