Detection and characterization of external bearing race structural anomalies via empirical wavelet transform spectral envelope

This research investigates the implementation of the Empirical Wavelet Transform (EWT) for advanced bearing fault detection in induction motors through sophisticated stator current signature analysis. The EWT algorithm represents a sophisticated signal processing methodology that generates adaptive wavelets by constructing optimized filter banks with Fourier support boundaries dynamically derived from the input signal's spectral content. The diagnostic protocol employs a dual-stage approach: first, decomposing the signal into modal components using EWT, and subsequently applying Hilbert envelope analysis to the terminal EWT mode to identify fault-specific frequency signatures. This innovative methodology addresses critical challenges in predictive maintenance by enabling precise detection of incipient mechanical anomalies. Experimental validation through comprehensive field measurements demonstrates the exceptional diagnostic capabilities of the hybrid EWT-Hilbert approach. The research reveals the method's superior sensitivity in detecting bearing defects across diverse operational conditions, highlighting its robust adaptability and reliability in real-world industrial environments. The proposed signal processing framework offers a transformative solution for continuous machinery health assessment, bridging advanced computational techniques with practical engineering diagnostics. By providing a data-driven, adaptive approach to mechanical fault detection, this research contributes significantly to the advancement of predictive maintenance strategies in rotating electrical machinery.