Improved Domain Adaptation Approach for Bearing Fault Diagnosis

Abstract

Application of domain adaptation techniques to predictive maintenance of modern electric rotating machinery (RM) has significant potential with the goal of transferring or adaptation of a fault diagnosis model developed for one machine to be generalized on new machines and/or new working conditions. The generalized nonlinear extension of conventional convolutional neural networks (CNNs), the self-organized operational neural networks (Self-ONNs) are known to enhance the learning capability of CNN by introducing non-linear neuron models and further heterogeneity in the network configuration. In this study, first the state-of-the-art 1D CNNs and Self-ONNs are tested for cross-domain performance. Then, we propose to utilize Self-ONNs as feature extractor in the well-known domain-adversarial neural networks (DANN) to enhance its domain adaptation performance. Experimental results over the benchmark Case Western Reserve University (CWRU) real vibration data set for bearing fault diagnosis across different load domains demonstrate the effectiveness and feasibility of the proposed domain adaptation approach with similar computational complexity.