Low-Speed Fault Diagnosis of Machinery Based on Improved Spectral Amplitude Modulation with Advanced Denoising Methods.

Heavy background noise inevitably masks the weak fault characteristics of low-speed bearings in offshore wind turbines, severely degrading the fault detection accuracy of conventional spectral amplitude modulation. To address this challenging issue, a novel hybrid diagnosis method integrating fast spectral coherence and spectral amplitude modulation is proposed for low-speed bearing fault detection. Firstly, spectral amplitude modulation is implemented on raw vibration signals to construct modified signal components. Subsequently, the fast spectral coherence algorithm is employed to eliminate complex noise interference and reconstruct high-quality enhanced envelope spectra. Further squaring transformation and amplitude normalization operations are conducted to effectively amplify incipient and weak fault signatures buried in noisy signals. Multiple visual analysis strategies are also adopted to intuitively present the fault diagnosis results. The effectiveness and superiority of the proposed method are comprehensively validated by simulated signals with varying signal-to-noise ratios and experimental data of bearing inner and outer race faults under a low rotating speed of 60 RPM. The results demonstrate that the proposed hybrid method significantly remedies the inherent limitations of traditional diagnosis methods, providing a robust and reliable technical solution for incipient fault monitoring and health assessment of low-speed bearings operating in harsh offshore wind field environments.