基于数据驱动思想，提出了一种相同工况下的滚动轴承寿命预测方法。针对轴承全寿命监测数据，根据K-means聚类算法划分轴承运行状态空间，考虑到隐马尔科夫模型主链为状态链的不足，对状态转移矩阵重新定义，将主链改进为寿命链，建立了基于改进HMM的全寿命状态驻留时间模型；将观测轴承数据、实时与建模数据进行Pearson相似度分析，构造寿命比例调节系数，实现寿命模型参数的动态修正和观测轴承寿命的自适应预测。采用美国辛辛那提大学实验中心轴承试验数据开展了应用研究，通过一组轴承全寿命数据实现了对其它轴承不同阶段及全寿命的预测，与传统的隐马尔科夫模型、灰色模型预测等方法预测结果相比，所提算法兼具较好的预测准确性和模型的泛化性。
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.
关键词
隐马尔科夫模型 /
寿命预测 /
Pearson相似度分析 /
滚动轴承
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Key words
hidden Markov model /
life prediction /
Pearson similarity analysis /
rolling bearing
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