基于ASFSSA-SVM的电机滚动轴承故障诊断OA

Aiming at the problems of the difficulty to extract the fault features from the bearing vibration signals and insufficient diagnosis accuracy,a fault diagnosis model was proposed.In this method,the feature vector was extracted from the optimized variational modal decomposition by using the adaptive spiral flying sparrow search algorithm,and used as the input to optimizes the support vector machine by means of the adaptive spiral flight sparrow search algorithmas for diagnostic recognition.First of all,the number of modes K and the penalty parameter of the adaptive spiral flight sparrow search algorithm were optimized for the variational modal decomposition,and the multiple Intrinsic Mode Function components were obtained by using VMD signal processing.Then,the optimal IMF components were screened out by using the craggy value as the evaluation index,and the mean value,square difference,peak value,peak factor,impulse factor,and waveform factor of the optimal IMF components were computed as the feature quantities.Finally,the fault identification classification was performed in the ASFSSA-SVM model.The experimental results show that this method possesses excellent diagnostic efficiency across different experimental data.