基于EMD和连续隐马尔可夫模型的滚动轴承故障诊断方法

Rolling Bearing Fault Diagnosis Method Based on EMD and Continuous Hidden Markov Model

鉴于滚动轴承微小损伤的振动信号难以分类和识别,提出了一种基于EMD和连续隐马尔可夫模型的滚动轴承故障诊断方法。该方法对分帧预处理后的信号进行经验模态分解,通过计算相关系数选择与原信号相关性较高的前五个IMF分量,然后从中提取能量、峭度和裕度构成特征向量,最后训练隐马尔可夫模型对测试信号故障识别。经由实验验证,所提出的方法可有效且准确地识别预先设定故障类型的待测轴承故障。

In view of the difficulty in classifying and identifying the vibration signals of small damages in rolling bearings,a fault diagnosis method for rolling bearings based on EMD and continuous hidden Markov model is proposed.This method perform empirical mode decomposition on the framed preprocessed signal,selects the first five IMF components with high correlation with the original signal by calculating correlation coefficients,and then extract energy,kurtosis and margin from these filtered IMF components to form feature vectors and finally the hidden Markov model is trained to identify the test signal failure.Through experimental verification,after pre-setting the fault type of the bearing fault,the method proposed in this paper can effectively and accurately identify the fault type of the bearing to be tested.