Deep domain adaptation for label-efficient and generalizable bearing fault diagnosis

This study presents the application of deep domain adaptation techniques in bearing fault diagnosis. Deep domain adaptation is a type of data-centric, label-efficient transfer learning approach based on deep neural network construction. Bearing fault diagnosis is a field which aims to perform analysis on the vibration signals generated from bearing apparatus. Different types of model structure and experiments are implemented on selected datasets, with associated ablation studies to analyze the effectiveness of domain adaptation compared to vanilla approaches. Our experiments show great performance and potential of the combination between bearing fault diagnosis and advanced machine learning techniques. In addition, detailed analysis also suggests the influence of the ways the bearing data are collected over the resulting performance, which leads to future directions for more accurate and specific approaches for bearing fault diagnosis tasks.