基于CMCPSO-SVM的轴承微弱故障诊断方法

针对旋转机械轴承微弱故障振动信号易被强噪声掩盖难以识别的问题,提出一种改进混沌粒子群优化支持向量机的故障诊断方法。将信号通过局部均值分解算法分解处理得到乘积函数(PF)分量,并进行能量归一化处理获得时频域特征集;通过迭代拉普拉斯得分降低时频域特征集的空间维度;以PF分量的排列熵作为混沌粒子群的适应度,并加入交叉和变异新策略,建立一种新的交叉变异混沌粒子群优化方法;利用改进的粒子群算法优化支持向量机的核函数和惩罚因子,并将优化后的分类模型应用于轴承故障诊断。结果表明:该故障分类模型的识别准确率高于其他分类模型。

Weak Bearing Fault Diagnosis Method Based on CMCPSO-SVM

Aiming at the problem that weak fault vibration signals of rotating machinery bearings are easily covered by strong noise and difficult to be recognized,an improved chaotic particle swarm optimization support vector machine fault diagnosis method was proposed.The signal was decomposed by using a local mean decomposition (LMD) algorithm to obtain the product function (PF) component,and the energy normalization process was performed to obtain the time-frequency domain feature set;the spatial dimension of the time-frequency domain feature set was reduced through iterative Laplacian score (ILS);the permutation entropy of the PF component was used as the fitness value of the chaotic particle swarm,and new crossover and mutation strategies were added to establish a new cross-mutation chaotic particle swarm optimization method;the kernel function and penalty factor of the support vector machine were optimized through the improved particle swarm algorithm,and the new classification model was applied to the fault diagnosis of wind turbine generator bearings.The results show that the recognition accuracy of this fault classification model is higher than other classification models.