Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis

This paper proposes a novel approach named adaptive multi-scale attention convolution neural network (AmaCNN) to accurately detect cross-domain faults with very few labelled data. In AmaCNN, multi-scale feature fusion CNN (MSFFCNN) with a multi-level attention scheme (MLAS) extracts multi-scale less-noise features from source and target domains. Considering the domain shift and semantic difference in the two domain features, a cross-domain adaption (CDA) scheme is applied. Significantly, the extracted domain features are measured with correlation alignment (CORAL) distance to minimize the domain shift first. Then, semantic alignment (SA) loss aligns and separates domain-invariant features point-by-point. Therefore, the proposed AmaCNN could learn rich multi-scale, less-noise, domain-invariant, and semantic-alignment features using limited training samples to detect cross- fault accurately. The experimental results on three real data sets confirmed its priority and reliability. Besides, the in-depth analysis has confirmed each component's effectiveness and CDA's good generality.