A Lightweight Complex-Domain Acoustic Feature Extraction Method for Rotating Machinery Fault Detection

Abstract In the field of fault diagnosis for factory machinery systems, the development of deep learning methods has been hindered by the challenge of acquiring fault data, highlighting the need to extract noise robust features from limited labeled data. In this paper, a light and efficient complex-domain acoustic feature extraction method, complex-domain principal frequency convolution network (CPFCN) is proposed for fault diagnosis in rotating machinery, which consists of a principal frequency filter (PFF) and stacked convolution network (SCN). The PFF filters out non-principal frequency noise to focus on the predominant frequency. The SCN is designed to effectively extract the amplitude and phase features, which can fully leverage the complex-domain information within the acoustic data. The experimental results show that the proposed CPFCN have 33% increasing in accuracy while 87% reduction in training time and 41% reduction in feature extraction time. Additionally, the proposed framework has improved the accuracy by 59% on the dataset with noise compared to the best-performing method in the experimental study, achieving stronger noise robustness in the case of limited samples.