Making transformer hear better: Adaptive feature enhancement based multi-level supervised acoustic signal fault diagnosis

Abstract

Acoustic signal fault diagnosis has been receiving increasing attention in the field of engine health management due to its effectiveness and non-invasiveness. Despite the progress made in fault diagnosis models, challenges still exist due to the complexity of acoustic signals and environmental factors. (1) End-to-end deep networks for fault diagnosis are at risk of underperformance or overfitting due to complex models and imbalanced data. (2) The complex acoustic environment within the vehicle power compartment poses obstacles to extracting subtle fault features. (3) Time–Frequency (TF) analysis has been proven to be an effective tool for characterizing the nonlinear features of fault signals, but it falls short in achieving ideal fidelity and resolution. To address these issues, an engine acoustic signal fault diagnosis method based on multi-level supervised learning and time–frequency transformation was proposed. First, adopting a multi-level supervised learning paradigm decomposes the fault diagnosis task into three stages: feature enhancement, fault detection, and fault identification, thereby incorporating additional experiential knowledge to mitigate overfitting. Second, an adaptive fault feature band extraction algorithm based on the fusion of multiple time–frequency analyses is proposed, specifically for extracting unique features from different vehicle datasets. Finally, a frequency band attention module was designed to focus on the frequency range most relevant to the characteristics of engine fault. The proposed method was validated on various audio signal fault datasets, and the results indicated its superior performance compared to other state-of-art fault detection and identification methods.