基于数学形态学分形理论与PNN的轴承状态监测与故障诊断

Bearing condition monitoring and fault diagnosis based on mathematical morphology and fractal theory and PNN

针对滚动轴承状态监测实时性差、故障诊断准确率低的问题,提出一种基于改进局部均值分解(ILMD)和数学形态学分形理论的特征提取算法,并结合概率神经网络(PNN)完成对轴承状态的智能化识别分类.该算法首先通过ILMD分解轴承原始振动信号,选取相关性系数最大的两阶分量,求取其分形维数作为特征向量;其次,结合盒维数理论,将“形态学覆盖面积”作为第三维特征向量,同时构建起三维特征矩阵;最后,将特征矩阵输入PNN以完成状态的识别分类.使用西储大学实测轴承数据验证算法,结果表明,该算法不仅能够精确识别不同状态的轴承,还能有效分类同种故障下不同损伤程度的轴承状态,平均识别率超过99.6%,平均识别时间0.21 s.

Aiming at the problems about poor real-time monitoring of rolling bearing condition and low accuracy of fault diagnosis,a feature extraction algorithm based on improved local mean decomposition(ILMD)and mathematical morphology fractal theory is proposed,and making it combine with probabilistic neural network(PNN)to complete the intelligent recognition and classification of bearing status.Firstly,the algorithm decomposes the original signal of the bearing through ILMD,selects the two-order component with the largest correlation coefficient and finds its fractal dimension as the feature vector.Secondly,the“morphological coverage area”is used as the third-dimensional feature vector.At the same time,a three-dimensional feature matrix is constructed.Finally,the feature matrix is input into PNN to complete the state recognition and classification.Using the actual bearing data of CWRU,the experiment results show that the proposed algorithm can not only accurately identify bearings in different states,but also effectively classify bearing states with different damage levels under the same failure.The average recognition rate exceeds 99.6%,and the average recognition time is 0.21 s.