An adaptive life prediction method for rolling bearings based on improved HMM and similarity calculation

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (8) : 172-177.

QU Jiaming1,ZHOU Yiwen1,WANG Heng1,HUANG Xi1,JIANG Jie2

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Abstract

Abstract:Based on the idea of data driven, a life prediction method for rolling bearing under the same working conditions was proposed.According to the bearing life monitoring data, the bearing operation state space was divided according to the K-means clustering algorithm, and based on the improved hidden Markov model, a full-life state duration time distribution model was established.The description state information and observation data were retained.On the basis of the chain structure, the description of the change law of bearing life is more suitable for actual situation.For the observation bearing data, based on state clustering, spatial translation and threshold matching, Pearson similarity analysis was performed in real time with the modeling data, and the life proportion adjustment coefficients were constructed according to the similarity analysis.Finally the hidden Markov life model parameters were dynamically modified to predict the observation of bearing life adaptively.Application research was carried out using the bearing test data of the University of Cincinnati Experimental Center.Through a set of bearing life data, the prediction of different stages and lifespan of other bearings was realized.Compared with the gray model prediction results, the proposed algorithm has better prediction accuracy and generalization of the model.

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hidden Markov model / life prediction / Pearson similarity analysis / rolling bearing

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QU Jiaming1,ZHOU Yiwen1,WANG Heng1,HUANG Xi1,JIANG Jie2. An adaptive life prediction method for rolling bearings based on improved HMM and similarity calculation[J]. Journal of Vibration and Shock, 2020, 39(8): 172-177

References

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