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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2015, Vol. 36 ›› Issue (2): 564-574.doi: 10.7527/S1000-6893.2014.0312

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Residual lifetime prediction for aeroengines based on Wiener process with random effects

LIU Junqiang, XIE Jiwei, ZUO Hongfu, ZHANG Malan   

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Received:2014-03-12 Revised:2014-11-05 Online:2015-02-15 Published:2014-11-15
  • Supported by:

    National Natural Science Foundation of China (61232002, 60939003); China Postdoctoral Science Foundation (2012M1081, 2013T60537); Postdoctoral Science Foundation of Jiangsu Province of China(1031107C); Fundamental Research Funds for the Central Universities (NS2014066); Philosophy and Social Science Research Projects in Colleges and Universities in Jiangsu (2014SJD041)

Abstract:

There are few models in consideration of the unit-to-unit variability and multi-phase variability simultaneously in the residual lifetime (RL) prediction for aeroengines, so propose a Wiener process-based degradation modeling method for RUL prediction considering the above-mentioned factors. First, this method models the degradation path for aeroengines conditioned on multi-phased Wiener process. Then, historical degradation data and failure-time data are fused to derive the prior distribution of the unknown parameters for degradation model and expectation maximization algorithm is used to estimate the hyper-parameters of the prior distribution. Once the real-time degradation data are available, posterior distribution of the parameters is updated through a Bayesian method. Lastly, the RL prediction is obtained based on the updated parameters. Experiment shows that the method can improve the accuracy of the RL prediction and can provide the decision-maker with enough information to perform necessary maintenance actions prior to the failure.

Key words: Wiener process, residual lifetime, information fused, expectation maximization algorithm, Bayesian method

CLC Number: