基于小波包和AFSA-SVM的电机故障诊断

针对电机滚动轴承故障诊断准确率问题,提出基于小波包分析结合人工鱼(AFSA)优化支持向量机(SVM)的电机故障诊断方法.利用小波包多分辨率分析法对电机的震动信号进行多层分解及重构,得到不同频段的信号时频图;然后采用AFSA算法对支持向量机中的参数惩罚参数(C)和核参数(σ)进行寻优选择,并最终建立AFSA-SVM故障诊...

Motor fault diagnosis based on wavelet packet and AFSA-SVM

Aiming at the problem of motor rolling bearing fault diagnosis accuracy, a motor fault diagnosis method based on wavelet packet analysis and artificial fish(AFSA) optimized support vector machine(SVM) was proposed. Wavelet packet multi-resolution analysis method was used to decompose and reconstruct the motor vibration signals, and the time-frequency diagrams of different frequency bands were obtained. Then, AFSA algorithm is used to optimize the parameter penalty parameter(C) and kernel parameter(σ) in the support vector machine. Finally, the fault diagnosis model of AFSA-SVM is established, and the frequency band energy value in the signal time-frequency graph is extracted as the input feature vector of AFSA-SVM for learning and testing. Finally, the simulation experiment verifies that the fault diagnosis accuracy is up to 98.7%, and compared with the particle swarm optimization support vector machine(PSO-SVM) and the unoptimized support vector machine, the results show that the proposed method has a higher fault diagnosis and recognition effect.