Architecture design and implementation for sensing equipment faults from ultra-weak acoustic emission signals
Year: 2025
Type: article
Abstract: In acoustic emission (AE) detection, the weakness of the acoustic source signal, the interference from background noise, and the attenuation during signal propagation result in the sensor-received signal being completely submerged by noise, severely impacting downstream fault identification and anomaly analysis. This study focuses on the fault detection scenario of AE signals characterized by a low signal-to-noise ratio (SNR). It uses low-loss noise reduction and Mel-frequency spectrum transformation for preprocessing, then extracts features with an optimized stacked auto-encoder combined with CNN (OSAE-CNN) innovatively. These features are input into an SVM for fault classification. The proposed method significantly improves fault identification accuracy to 91.38% for signals with an SNR of −20 dB, a 30% increase over the previous method. The research findings can provide technical support for the fault monitoring and safe operation of electromechanical equipment and also offer empirical references for ultra-weak signal processing in various domains.
Source: Sound&Vibration
DOI: 10.59400/sv2757
Authors: Li'an Lu, Guangyu Liu, Hui Xiao, Xuefeng Li
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409648960