Fourier Feature Refiner Network With Soft Thresholding for Machinery Fault Diagnosis Under Highly Noisy Conditions
Huan Wang, Wenjun Luo, Zhiliang Liu, Junhao Zhang, Ming J. Zuo
Abstract
Machinery fault diagnosis plays an important role in machine Prognostic and Health Management (PHM). Leveraging the abundant data obtained from the Industrial Internet of Things (IIoT), the health states of machines can be effectively recognized, thereby ensuring the safety of the mechanical system. However, the lack of noise robustness and insufficient frequency domain perception make traditional methods to extract weak fault-related signals difficult under highly noisy conditions in practical industrial scenarios. Therefore, a method with abundant frequency domain learning ability is urgently needed. To this end, this paper proposes a PHM framework, a soft thresholding Fourier feature refiner network (Soft-FFRNet), for highly noisy bearing vibration signal diagnosis. Specifically, this framework includes a Fourier feature refiner which selectively extracts and refines the feature in the frequency domain from the perspectives of amplitude and phase. It achieves the extension from the time domain to the frequency domain. In addition, the proposed framework utilizes several residual blocks with soft thresholding to effectively improve the noise robustness. Their thresholds can adaptively change during the training process. The high-speed aeronautical (HSA) bearing and the motor bearing datasets with different noise levels are used to evaluate this framework. The results show that the proposed framework can effectively diagnose the faults under highly noisy conditions.