基于隐马尔可夫的滚动轴承健康状态评价方法研究

Evaluation of health status of rolling bearing based on hidden Markov

滚动轴承是发电机内部最容易发生故障的部件之一,为了评价轴承的运行状态,保证发电机组的正常运行,提出了一种基于小波包-互补聚合经验模态分解(CEEMD)和变分贝叶斯-隐马尔可夫模型(HMM)的滚动轴承健康状态评价方法。首先,采用小波包-互补聚合经验模态分解对振动信号进行了降噪和特征提取,再将提取到的健康状态下的特征信号输入到变分贝叶斯-隐马尔可夫模型中进行了训练,最终将轴承的全周期特征信号输入到该模型中,分别用轴承退化实验数据和实验台实测数据对此模型进行了测试。研究结果表明:模型输出的对数似然概率值可作为指标进行状态评价;对数似然概率曲线的变化均能反映轴承的健康、劣化和失效的运行状态,该实验方法具有很好的适用性。

The rolling bearing was one of the most prone to failure components inside the generator.In order to evaluate the operating status of the bearings and ensure the normal operation of the generator set,based on wavelet packet complementary aggregation empirical mode decomposition and variational Bayes hidden Markov model,a method for rolling bearing health status evaluation was proposed.First,the wavelet packet-complementary empirical mode decomposition for signal denoising and feature extraction was used.The extracted feature signals in a healthy state were input into the variational Bayes-Hidden Markov model for model training.Then the characteristic signal of the full-period bearing was input into the model.The model was tested with bearing degradation experimental data and experimental rig measured data.The research results show that the log-likelihood probability value output by the model can be used as an indicator for state evaluation.The changes in the log-likelihood probability curve can reflect the health,deterioration and failure of the bearing.Therefore,the experimental method has good applicability.