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Journal of Control Science and Engineering
Volume 2017 (2017), Article ID 4375690, 11 pages
Research Article

Degradation Data-Driven Remaining Useful Life Estimation in the Absence of Prior Degradation Knowledge

1Department of Automation, Xi’an Institute of High-Technology, Xi’an, Shaanxi 710025, China
2Institute No. 25, The Second Academy of China Aerospace Science and Industry Corporation, Beijing 100854, China

Correspondence should be addressed to Changhua Hu

Received 6 August 2017; Accepted 23 October 2017; Published 3 December 2017

Academic Editor: Chunhui Zhao

Copyright © 2017 Yong Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent developments in prognostic and health management have been targeted at utilizing the observed degradation signals to estimate residual life distributions. Current degradation models mainly focus on a population of “identical” devices or an individual device with population information, not a single component in the absence of prior degradation knowledge. However, the fast development of science and technology provides us with many kinds of new systems, and we just have the real-time monitoring information to analyze the reliability for them. The fusion algorithm presented herein addresses this challenge by combining the excellent modeling ability of Bayesian updating method for the multilevel data and the prominent estimation ability of ECM algorithm for incomplete data. Residual life distributions and posterior distributions are first calculated through the Bayesian updating method based on random initial a priori distributions. Then the a priori distributions are revised and improved for future predictions by the ECM algorithm. Once a new signal is observed, we can reuse the fusion algorithm to improve the accuracy of residual life distributions. The applicability of this fusion algorithm is validated by a set of simulation experiments.