Complex Symplectic Geometry Mode Decomposition and A Novel Time–Frequency Fault Feature Extraction Method

Although the condition monitoring of sophisticated equipment plays a crucial role in practical applications, the fault symptom is often overwhelmed by the harsh working conditions with compound and unknown interfering noise. To address this challenge, this paper introduces a novel signal decomposition scheme, termed the Complex Symplectic Geometry Mode Decomposition (CSGMD), that leverages the instantaneous complex envelope to ensure the complex statistical structure hidden in a signal. First, the applicable condition of the symplectic similar transform theory is extended into the complex domain to facilitate the exploitation of the phase information. Relying on phase-corrected Short-Time Fourier Transform, the CSGMD achieves a time-frequency signal decomposition while enhancing the interpretation of the physical significance of the feature extraction. Target component reconstruction is then executed by using the statistical indicators, resulting in the reconstructed components that are enriched with the original characteristic information. The effectiveness of the proposed method is finally demonstrated with both simulated and measured signals. Through the comparative experiments of the state-of-the-art methods, its superiority of feature extraction and signal decomposition is further verified with non trivial cases of complex and strong noise interference.