Aimed at the deficiency of the Hidden Markov model whose hidden states must be determined in advance, a prognostics method of mechanical equipment based on the Hierarchical Dirichlet Process-Hidden Markov model (HDP-HMM) was proposed.By constructing HDP as the prior distribution of HMM, the structure of HMM was dynamically adjusted and the state number during the operation of the equipment degradation was obtained according to hierarchical sharing and automatic clustering of HDP.Based on the dynamic transition state relationship established by HDP-HMM, the early failure point and functional failure point of the equipment were determined, and the health grade evaluation and prognostics of the equipment were realized.The application of life data of rolling bearings provided by the USFI/UCR intelligent maintenance system center was studied.The results show that HDP-HMM can effectively achieve the combination clustering for multiple observation sequences and the recognition results do not depend on the choice of initial parameters of the algorithm which has strong robustness.Compared with the K-S test algorithm of degradation assessment, HDP-HMM can describe the actual degradation process of the equipment more effectively.
王恒,周易文,瞿家明,季云. 基于HDP-HMM的机械设备故障预测方法研究[J]. 振动与冲击, 2019, 38(8): 173-179.
WANG Heng, ZHOU Yiwen, QU Jiaming,JI Yun. A prognostic method of mechanical equipment based on HDP-HMM. JOURNAL OF VIBRATION AND SHOCK, 2019, 38(8): 173-179.
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