多小波系数特征提取方法在故障诊断中的应用

针对机械故障的特征提取问题,提出一种基于多小波系数的机械故障特征提取方法。首先,对不同工况的机械振动信号进行多小波分解;其次,利用分解后各层多小波系数的统计特征包括最大值、最小值、均值和标准差作为该工况振动信号的特征向量;最后,利用支持向量机的方法对机械故障进行识别。对滚动轴承正常状况与内圈故障、滚动体故障、外圈故障3种故障及多种损伤程度的实测振动信号进行故障识别试验,试验结果表明,该方法用于机械故障诊断可以获得较高的识别率,识别效果要优于基于单小波系数统计特征的识别方法,具有一定的工程应用价值。

Application of Feature Extraction Method in Fault Diagnosis Based on Multi-Wavelet Coefficients

Aimed at feature extraction in machinery fault diagnosis, this paper proposes a new fault feature extraction method based on multi-wavelet coefficients. The original vibration signals of each fault category are decomposed into time-frequency representations using multi-wavelet transform. Then the maximum, minimum, mean and standard deviation of the multi-wavelet coefficients in each subband are calculated and used as the feature vector. The support vector machine method is used for machinery fault classification. Experiments are conducted on the real vibration signal of the roller bearing with normal conditions, inner fault, ball fault and outer fault. The experimental results indicate that the proposed approach can reliably identify the different fault categories, works better than the single wavelet method, and thus has potential for machinery fault diagnosis.