Review of Application of Convolutional Neural Networks in Mechanical Fault Diagnosis
Convolutional Neural Network(CNN)have been widely used in the field of mechanical fault diagnosis in recent years because of their advantages in image recognition and classification.Due to the superiority of CNN in extracting fault features,it greatly promotes the development of mechanical fault diagnosis technology.However,the current problems such as unbalance of sample data,noise interference and unexplainability of the model greatly hinder the development of CNN technology in the field of fault diagnosis.In order to further improve the performance of the model,based on the research progress of CNN mechanical fault diagnosis model in recent years,this paper classifies and summarizes the CNN model framework of mechanical fault diagnosis,and then discusses and analyzes the progress of solving sample imbalance and interpretability problems.Finally,the development direction of CNN in the field of mechanical fault diagnosis is prospected.
NETL
NSTL
万方数据
