A Dense ResNet Model with RGB Input Mapping for Cross-Domain Mechanical Fault Diagnosis

Abstract

In actual engineering applications, the mechanical machine is exposed to uncertain conditions such as noise interference and various loads. The commonly used fault diagnosis models suffer degradation in the prediction accuracy in such complex industrial environments where the available label samples are insufficient and the conditions are varied. To combat this challenge, a cross-domain mechanical fault diagnosis method based on the deep-learning networks is proposed. It utilizes small samples, i.e., 10% of the total, and operates on the time-series signal collected from the mechanical equipment. It provides a classification accuracy of more than 97% on the dataset from Case Western Reserve University (CWRU) under variable conditions and 97.56% with the noise interference of 0 dB. The one-dimensional vibration signal is first converted into an image through RGB mapping. Then, the derived RGB image is capable of the time dependent and spatial properties of the time sequence signal and can be directly used as the input of the deep-learning networks. The deep-learning networks model, i.e., the ResNet, is adopted for the fault feature extraction and additional dense connections are added among the residual blocks to supplement the insufficient labeled samples within the networks. Then, an RGB-DResNet is constructed, capable of retaining the robust features for the classification of the mechanical faults in different working conditions. Finally, through retraining the model by use of transfer learning, the derived RGB-TDResNet model gives a fine adaption to the feature distribution with a small amount of target domain information. The performance of the proposed fault diagnosis model was validated on the dataset from CWRU. The results show that it provides a high identification accuracy and strong robustness in variable operating conditions as well as the noise environment. It is a rather promising approach for dealing with the cross-domain tasks of mechanical fault diagnosis.