Vibration Analysis for Bearing Fault Detection: A Comparison of Thomson Multitaper Periodogram, Welch’s periodogram, and Gaussian Filter Bank Algorithms.

Abstract: One of the most effective techniques in condition monitoring is vibration analysis. This paper presents a comprehensive approach to bearing fault detection using vibration analysis, leveraging advanced signal processing techniques. The study focuses on three primary algorithms: Gaussian Filter Bank, Welch's Periodogram, and Thomson’s Multitaper Periodogram. Each algorithm's efficacy is evaluated in terms of its ability to identify and isolate characteristic fault frequencies in bearing vibration signals. The Gaussian Filter Bank is employed for its superiorfrequency resolution and adaptability to nonstationary signals. Welch's Periodogram is utilized for its robust performance in estimating power spectral density with reduced noise variance. Thomson’s Multitaper Periodogram is chosen for its enhanced spectral estimation capabilities and reduced spectral leakage. Experimental results demonstrate that these algorithms, when applied to vibration data, can effectively detect and diagnose various types of bearing faults, offering significant improvements in predictive maintenance strategies.