Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model

Zhidong Sheng, Qingpei Hu, Jian Liu, Dan Yu

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The performance of certain critical complex systems, such as the power output of ground photovoltaic (PV) modules or spacecraft solar arrays, exhibits a multi-phase degradation pattern due to the redundant structure. This pattern shows a degradation trend with multiple jump points, which are mixed effects of two failure modes: a soft mode of continuous smooth degradation and a hard mode of abrupt failure. Both modes need to be modeled jointly to predict the system residual life. In this paper, an autoregressive moving average model-filtered hidden Markov model is proposed to fit the multi-phase degradation data with unknown number of jump points, together with an iterative algorithm for parameter estimation. The comprehensive algorithm is composed of non-linear least-square method, recursive extended least-square method, and expectation–maximization algorithm to handle different parts of the model. The proposed methodology is applied to a specific PV module system with simulated performance measurements for its reliability evaluation and residual life prediction. Comprehensive studies have been conducted, and analysis results show better performance over competing models and more importantly all the jump points in the simulated data have been identified. Also, this algorithm converges fast with satisfactory parameter estimates accuracy, regardless of the jump point number.

Original languageEnglish (US)
Pages (from-to)1-17
Number of pages17
JournalQuality Technology and Quantitative Management
DOIs
StateAccepted/In press - Jun 9 2017

Fingerprint

Hidden Markov models
Large scale systems
Degradation
Parameter estimation
Failure modes
Spacecraft
Prediction
Autoregressive moving average
Jump
Hidden Markov model
Complex systems
Least square method
Module

Keywords

  • hidden Markov model
  • multi-phase degradation
  • residual life prediction
  • System reliability

ASJC Scopus subject areas

  • Business and International Management
  • Industrial relations
  • Management Science and Operations Research
  • Information Systems and Management
  • Management of Technology and Innovation

Cite this

Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model. / Sheng, Zhidong; Hu, Qingpei; Liu, Jian; Yu, Dan.

In: Quality Technology and Quantitative Management, 09.06.2017, p. 1-17.

Research output: Contribution to journalArticle

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