A multi-feature fusion-based domain adversarial neural network for fault diagnosis of rotating machinery

Deep learning (DL)-based Fault Diagnosis (FD) methods have been wildly used in the industry domain for the guarantee of rotating machinery. Training these models often deserver abundant labeled data from complex or variable working conditions. However, it is knotty to obtain massive data of different types of faults for the working condition of interest in engineering practice which also greatly hinders the improvement of DL-based FD methods. In addition, exiting DL-based method could not achieve satisfactory diagnosis results when the working condition between source-domain (training data) and target-domain (testing data) is different. This paper proposes a novel FD method using multi-feature fusion scheme and an Improved Domain Adversarial Neural Network (IDANN). Firstly, the Fast Fourier Transform (FFT) is utilized for time-to-frequency domain conversion of raw signals. Then, the multi-feature fusion scheme is adopted to fuse the spectral samples with different working conditions, which uses multi-branch convolution layers as feature extractor and fuser. After that, the fused features are fed into IDANN as input, and the adversarial training strategy is used to train the IDANN model until an ideal equilibrium state is achieved. Finally, the feature extractor and label predictor are separated from the trained IDANN model for classification of health conditions. To verify the performance of IDANN, two public bearing datasets from Case Western Reserve University (CWRU) and Paderborn University are utilized, and results show that IDANN achieves superior diagnosis performance by making full use of multi-source of signal data compared with other conventional or DL-based diagnosis methods.