EEMD and EMD: Advanced Fault Diagnosis Methods for Signal Extraction and Analysis

Fault diagnosis based on Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) represents an innovative methodology that effectively extracts fault signals and enables precise diagnostics. This approach leverages the combined strengths of EEMD and EMD to better handle complex fault scenarios through adaptive signal decomposition. The implementation typically involves decomposing input signals into intrinsic mode functions (IMFs) using sifting processes, where EEMD incorporates noise-assisted analysis to mitigate mode mixing issues. Through systematic signal decomposition and analytical processing, practitioners can extract comprehensive fault characteristic information, significantly improving diagnostic accuracy and reliability. Key algorithmic steps include initializing ensemble sizes, applying white noise sequences, and performing Hilbert-Huang transforms for time-frequency analysis. These methods have found extensive applications across multiple domains including industrial automation, power systems, and mechanical equipment. Consequently, EEMD and EMD-based fault diagnosis presents a highly promising and effective approach that facilitates deeper understanding and resolution of diverse fault conditions through computational signal processing techniques.