Prototypical Normalized Output Domain Adversarial Adaptation Network for Fault Diagnosis with Fusion Perception

Deep learning has made significant strides in fault diagnosis when training and testing are conducted within domains of the same distribution. However, data collected from actual industrial equipment often originates from varying load operating conditions, leading to shifts in the manifestation of fault patterns. Additionally, non-stationary signals tend to exhibit similarities or weak fault frequencies in the frequency domain, making them difficult to distinguish. To address these problems, a prototypical normalized output domain adversarial adaptation network (PNOAN) with local and full frequency fusion perception is proposed. Initially, the vibration signal is transformed to time frequency graph by Morlet wavelet. Then, a band pooling module is employed to learn the long-range local context of axial time and frequency in time frequency graph to achieve local perception of fault signal, while a dual-part attention mechanism focuses on both high and low frequency information to achieve full frequency perception. Furthermore, an improved domain adversarial neural network is designed to standardize the conditional alignment of features and prediction probabilities for domain adaption fault diagnosis. Extensive results on a rotating bearing dataset illustrate the effectiveness and superiority of the proposed PNOAN.