Remaining Useful Life Estimation of Mechanical Systems Based on the Data-driven Method and Bayesian Theory

doi:10.3901/JME.2018.12.115

Abstract

Abstract: A novel remaining useful life(RUL) estimation method is proposed based on the data-driven method and Bayesian theory for the remaining useful life estimation of complex mechanical systems. Firstly, the condition monitoring data of same or similar systems are fused to form the Health Index indicating the degradation degree of systems and the state model by the data-driven method. Then, a Bayesian model of the state model parameters is built on Bayesian theory. With the on-line condition monitoring data of system to be estimated and the Bayesian model, the model parameters are updated by Markov Chain Monte Carlo (MCMC) and the RUL of system is estimated. At last, a turbofan engine case is used to show the effectiveness of the present method.

Key words: Bayesian model, data-driven, remaining useful life

CLC Number:

TG156

ZHAO Shenkun, JIANG Chao, LONG Xiangyun. Remaining Useful Life Estimation of Mechanical Systems Based on the Data-driven Method and Bayesian Theory[J]. Journal of Mechanical Engineering, 2018, 54(12): 115-124.

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