摘要详情

ID / 提交时间

306 / 2025-06-20 17:44:38

标题

A Feature Enhancement Method for Microphone Array based on Wavelet Domain Polynomial Eigenvalue Decomposition

关键字

rotating machinery,microphone array,polynomial eigenvalue decomposition,wavelet transform,fault diagnosis

主题及专题

4、System Monitoring, Fault Diagnosis and Prediction

状态

全文待审

作者

Shichao Zhang / Beijing University of Chemical Technology

Gang Tang / Beijing University of Chemical Technology

摘要

Acoustic signals contain critical information about the operating condition of rotating machinery. Due to their ease of installation and minimal environment constraints, microphone arrays have become an important non-contact monitoring technique. However, traditional beamforming methods rely on accurate estimation of the target signal direction, and often exhibit low diagnostic accuracy and poor adaptability in complex noise environments. To address these limitations, this paper proposes a fault diagnosis method for microphone array based on wavelet-domain polynomial eigenvalue decomposition (PEVD). This method eliminates the need to estimate the direction vector of the target signal and enables fault diagnosis without requiring any prior information. First, the collected time-domain array signals are converted into the wavelet domain using wavelet transform (WT). Subsequently, PEVD is performed on the wavelet coefficients at each decomposition layer, and the Gini index is employed as a metric to evaluate the signal characteristics. Finally, envelope spectrum analysis is applied to the reconstructed signals to identify fault characteristics. Simulation and experimental results demonstrate that the proposed method can effectively extract fault features in complex noise environments, significantly enhancing fault diagnosis performance.