Bearing fault diagnosis using Gradual Conditional Domain Adversarial Network

Given the limited availability of accurately labeled data in fault diagnosis across various industrial scenarios, we proposed a Gradual Conditional Domain Adversarial Network (GCDAN) incorporating various fault categories and rotating speeds. We constructed a prototype system for collecting three-dimensional vibration data samples and modified the network structure to accommodate the input. Inspired by the generalization capability of cross-device scenarios, we adopted CDAN as the main component. To overcome the performance degradation caused by the source and target domains with substantial distribution differences, we introduced Gradual Domain Adaptation into our algorithm. Unlabeled data samples obtained from the intermediate domains were used to train a sequence of CDANs. Experimental comparison results confirmed the effectiveness of the 3-D input data and its network alteration. Additionally, GCDAN performed better over challenging transfer tasks compared to the existing state-of-art algorithms in terms of prediction accuracy and multi-class classification metrics.