Application of a multi-dimensional synchronous feature mode decomposition for machinery fault diagnosis.

Abstract

Fault diagnosis in complex industrial systems often encounters significant challenges, including high noise levels, stochastic interference and coupled multi-fault features, especially for multi-channel signal processing. To address these issues, this study proposes multi-dimensional synchronous feature mode decomposition (MSFMD) method, a novel approach that combines multi-channel signal synergy with advanced decomposition and feature extraction techniques. The MSFMD method operates through a systematic framework comprising three key steps: custom-designed spectral segmentation strategy based on order statistic filter, synchronized decomposition of multi-channel signals with spectral alignment constraint, adaptive mode screening based on time-frequency correlation coefficients and envelope spectral kurtosis. Tailored for the channel signal, initial filter banks are decided. Then, the same fault-feature-oriented modes keep the spectral alignment constraint across channels, capturing inter-channel correlations while reducing noise and redundant modes. The adaptive screening strategy selectively retains fault-relevant modes, significantly improving the robustness and interpretability of the extracted features. MSFMD is able to effectively amplify weak fault features, handle complex multi-fault conditions, and improve computational efficiency under high-noise environments. Compared to traditional methods such as feature mode decomposition (FMD) and multivariable variational mode decomposition (MVMD), MSFMD demonstrates superior performance, such as susceptibility to noise, redundancy, and inefficiency in multi-fault scenarios. Validation through complex fault experiments confirms MSFMD's capability to provide accurate and reliable diagnostics.