A hybrid deep-learning model for fault diagnosis of rolling bearings

Detection accuracy of bearing faults is crucial in saving economic loss for industrial applications. Deep learning is capable of producing high accuracy for bearing fault diagnosis; however, in most of existing deep-learning models such as a convolutional neural network (CNN) model or a deep forest (gcForest) model, the fault feature extraction process is ignored. In order to address this issue, this study develops a hybrid deep-learning model based on CNN and gcForest. In this new method, bearing vibration signals were converted into time-frequency images using the continuous wavelet transform (CWT). Then, CNN was used to extract intrinsic fault features from the images and feed them into a gcForest classifier. Experimental bearing data provided by Case Western Reserve University (CWRU) and Xi'an Jiaotong University (XJTU-SY) were used to evaluate the performance of the proposed method. The analysis results demonstrated that the proposed hybrid deep learning model can achieve higher detection accuracy than CNN and gcForest, which may be favorable to practical applications.