Spectrogram Zeros Method for Rolling Bearing Fault Diagnosis Under Variable Rotating Speeds

Diagnosing rolling bearing faults is critical for maintaining machinery reliability, as these components are essential in reducing friction in rotating systems. The increased bearing failure rates at higher rotational speeds underscores the need for advanced diagnostic techniques. Traditional methods like generalized demodulation and order tracking require additional hardware, increasing complexity and cost. This study introduces a novel approach called SZC-SST, which uses spectrogram zeros classification and the Fourier-synchrosqueezing transform (FSST) to diagnose rolling bearing faults under variable speeds. The proposed method leverages the Hilbert transform to enhance the feature extraction, ensuring accurate fault detection. Experimental results validate the effectiveness of the method, demonstrating significant improvements in fault identification and noise reduction compared to conventional techniques. Integrating these advanced signal processing techniques provides a robust framework for early fault detection, potentially preventing catastrophic machinery failures.