Micro-motor Fault Classification Based on ALIF-GAF-AlexNet
Micro motor is an important power element,its diagnosis process is not complicated,but it is unreasonable to invest a lot of manual sorting,which will bring inefficient and one-sided diagnosis results.In order to improve the diagnostic efficiency and practica-bility of micro motor,a diagnostic method was proposed.The adaptive local iterative filtering method was used to reduce the noise,then the Gram angle field was used to convert the input sound signal after feature extraction into an image.The converted images were classi-fied by deep convolutional neural network model.The efficiency of the proposed method was evaluated by the data set collected in the ex-periment.The results show that this method has higher classification accuracy than other methods,the accuracy achieves 94.1％.
万方数据
维普
