Intelligent Fault Diagnosis of Rolling BearingBased on Deep Transfer Learning Using Time-Frequency Representation

Abstract

With the expansion of deep learning (DL) and machine learning (ML) methods, fault diagnosis based on data-driven models has recently become controversial. However, due to the lack of sufficient labeled data in fault diagnosis, the depth of proposed DL models is less than other models in computer vision areas, which decreases the generalization and accuracy of models. Deep transfer convolutional neural network (DTCNN) with powerful feature extracting is used to tackle this dilemma. In this study, DenseNet201, ResNet152V2 and, MobileNetV2 are chosen as DTCNN models for feature extraction. Firstly, vibration signals are converted into time-frequency RGB images by continuous wavelet transform (CWT). Then, the high-level features of images are extracted by the DTCNN models. Finally, different types of bearing faults are classified by DL and ML classifiers. The experiment is validated on the famous Case Western Reserve University (CWRU) bearing data set. The result demonstrates that the proposed DTCNN models achieve the best accuracy rate in the classification task and are faster to learn than many other existing DL and ML models.