基于多维振动特征的滚动轴承故障诊断方法

单独提取滚动轴承振动信号的时域或频域特征进行故障诊断，是目前常用的轴承诊断方法，诊断精度有待提高。以时域和频域的多维振动特征参量为指标，以历史诊断正确率作为特征参量权值，分别对滚动轴承的无故障和经常出现的滚珠故障、内环故障和外环故障工况进行特征提取和故障识别。多维时频域振动特征是单维特征依据诊断精度权重的集合。运用BP神经网络分别对信号的时域特征(TDF)、IMF能量矩(IEM)、小波包能量矩(WPEM)，以及多维时频域特征进行智能故障判别。实验验证用多维时频域振动特征参量综合诊断的方法进行滚动轴承故障诊断，比单维特征的诊断结果精确且效率较高，该方法可以在滚动轴承故障诊断领域展开应用。

Fault Diagnosis Method of Rolling Bearings Based on Multi-dimensional Vibration Features

Extracting the time-domain or the frequency-domain features of vibration signals for analysis is a conventional method for rolling bearings fault diagnosis. But the effects of this diagnosis method need to be improved. In this paper, taking the multi-dimensional vibration characteristic parameters in time-domain and frequency-domain as the indexes and the correctness rate of historical diagnosis as the parametric weight, the features of fault-free rolling bearings and the features of rolling bearings with ball fault, inner and outer race faults are extracted and the faults are identified. It shows that the multi-dimensional vibration characteristic in time-frequency domains is the assemblage of single features. BP neural network is used for intelligent fault classification of signals according to the time-domain feature （TDF） parameters, IMF energy moment （IEM）, wavelet package energy moment （WPEM） and multi-dimensional features respectively. Results of the diagnoses are compared one another. The experiment results verify that using the multi-dimensional feature in time and frequency domains to evaluate the rolling bearing faults is accurate and efficient. This method can be applied in the field of rolling bearing fault diagnosis.