摘要
Abstract
It is difficult to extract the characteristics of rolling bearings in industry,and the traditional diagnosis method requires the expertise of professionals and is difficult to ensure the accuracy of fault diagnosis.In order to solve the above problems,this paper combines convolutional neural network(CNN)with support vector machine(SVM)classification to proposes a fault diagnosis model for rolling bearings.The proposed model uses a combination of one-dimensional CNN and two-dimensional CNN,and the original fault bearing signal as input,and the fault signal characteristic value of rolling bearing is extracted by CNN.Then one-dimensional convolutional network output and the two-dimensional convolutional network output are spliced together,finally,the fault classification is completed by SVM classifier.In order to verify the proposed fault diagnosis model,a network model is built based on SGD,and typical fault categories such as inner ring fault and outer ring fault in CWRU data set are sorted,and normalization is used to increase the model generalization ability.Experimental verification is conducted to show that the proposed method has a diagnostic accuracy of up to 99.25%,able to realize the adaptive extraction of rolling bearing fault features,and has better recognition ability compared with the traditional feature extraction method used alone.关键词
滚动轴承/CNN/故障诊断/SVM/深度学习Key words
rolling bearing/CNN/fault diagnosis/SVM/deep leaning分类
机械制造
