An innovative approach to vibration signal denoising and fault diagnosis using attention-enriched joint learning

Abstract

Vibration signals play a crucial role in mechanical fault diagnosis. However, they are susceptible to various noise disturbances, presenting challenges for reliable fault detection. We propose an end-to-end Cross-task Attention Joint Learning (CTA-JL) model that concurrently denoises and diagnoses faults in noisy signals. This model utilizes a multi-task encoder, composed of task-shared and task-specific feature encoding units, along with a feature information exchange unit with a Cross-task Attention (CTA) mechanism, fostering information exchange across different tasks. By collectively executing diagnosis and denoising tasks and sharing valuable task information, the model enhances prediction accuracy and denoising performance. Under three noise conditions of SNR = −9 dB, −6 dB, and −3 dB, the prediction accuracy of CTA-JL on the rolling bearing datasets reached 91.38%, 97.95%, and 99.69%, respectively. Meanwhile, the result on elevator guide system datasets reached 87.31%, 95.58%, and 99.64%