Intelligent fault diagnosis in rolling element bearings: Combining envelope spectrum and spectral kurtosis for enhanced detection

• Hybrid envelope spectrum and spectral kurtosis method achieves 95%+ bearing fault detection accuracy. • Log-ratio classifier distinguishes inner, outer, and normal states in noisy, variable conditions. • Kurtogram-guided filtering enables real-time, scalable, and efficient fault detection. • Tests confirm robustness, noise resistance, and predictive maintenance suitability. Rolling-element bearings are vital components in industrial machinery, and their early fault detection is essential to prevent costly failures and downtime. This paper presents a robust and scalable fault diagnosis method that combines Envelope Spectrum Analysis (ESA) and Spectral Kurtosis (SK) to enhance detection accuracy under noisy and dynamic conditions. ESA identifies characteristic fault frequencies such as BPFO (Ball Pass Frequency Outer Race) and BPFI (Ball Pass Frequency Inner Race), while SK detects…