Research and Implementation of Bearing Fault Detection Algorithm Based on Acoustic Signal
Rolling bearing is an important part in mechanical equipment,compared with other parts of machinery,its life span is very discrete.The fault detection and diagnosis of bearing is studied,and acoustic signal-based bearing fault detection algorithm is designed.Firstly,the process of the fault detection system is proposed,including feature extraction,feature processing and selection,model training algorithms and optimization methods.Then,by using the processed features,a binary classification model,which is capable of distinguis-hing inner ring bearing faults from normal bearings along with the normal bearing with grease sound,is trained by means of neural net-works.The accuracy of the model on the test set reaches 97.8％.When testing the remaining data,the accuracy is 97.5％for the inner ring faulty bearing,97.3％for the normal bearing,and 98％for the normal bearing with grease sound.It shows that the algorithm has ex-cellent performance in bearing fault detection.
bearing fault detectionacoustic signal algorithmneural network
万方数据
维普
