A Generic Single-Source Domain Generalization Framework for Fault Diagnosis via Wavelet Packet Augmentation and Pseudo-Domain Generation
Yawei Sun, Hongfeng Tao, Yuanzhi Ni, Vladimir Stojanović
Abstract
During real-time production in industrial Internet of Things systems, equipment changes its operating speed due to changing operating conditions. And dynamic speed changes of rotating machinery under fluctuating workloads often lead to domain changes of vibration signals, which will directly lead to degradation of fault diagnostic model performance. Furthermore, the acquisition of data from multiple domains in real industrial scenarios is challenging due to the expense of collecting data from all possible working conditions. Consequently, applying diagnostic models trained using a single-source domain directly to an unknown target domain is a very challenging single domain generalization problem. Therefore, a generic single-source domain generalization framework via wavelet packet augmentation and pseudo-domain generation for fault diagnosis under unknown operating conditions is proposed in this paper. Pseudo-domain generation involves augmenting single-source domain by integrating data generetion model, thereby enhancing prediction accuracy. Furthermore, a wavelet packet augmentation method is proposed. Initially, the original signal is decomposed to obtain high and low frequency information. Subsequently, the high and low frequency information within the batch are linearly interpolated, respectively. Consequently, the interpolated high and low frequency information is then reconstructed to yield enhanced samples. The experimental results on four datasets show that the proposed framework can effectively improve the robustness of the generalization ability of fault diagnosis under unknown operating environments.