The use of SESK as a trend parameter for localized bearing fault diagnosis in induction machines. (July 2016)

• Record Type: Journal Article
• Authors: Saidi, Lotfi; Ben Ali, Jaouher; Benbouzid, Mohamed; Bechhoefer, Eric
• Is Part Of: ISA transactions. Volume 63(2016:Jul.)
• Page Start: 436
• Page End: 447
• Publication Date: 2016-07
• Subjects: Bearing failures -- Squared envelope analysis -- Spectral kurtosis -- Kurtogram -- Vibration; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.02.019
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: A critical work of bearing fault diagnosis is locating the optimum frequency band that contains faulty bearing signal, which is usually buried in the noise background. Now, envelope analysis is commonly used to obtain the bearing defect harmonics from the envelope signal spectrum analysis and has shown fine results in identifying incipient failures occurring in the different parts of a bearing. However, the main step in implementing envelope analysis is to determine a frequency band that contains faulty bearing signal component with the highest signal noise level. Conventionally, the choice of the band is made by manual spectrum comparison via identifying the resonance frequency where the largest change occurred. In this paper, we present a squared envelope based spectral kurtosis method to determine optimum envelope analysis parameters including the filtering band and center frequency through a short time Fourier transform. We have verified the potential of the spectral kurtosis diagnostic strategy in performance improvements for single-defect diagnosis using real laboratory-collected vibration data sets. Highlights: Vibration signals are commonly used for bearing condition monitoring. A critical work is locating the optimum frequency band that contains faulty bearing signal. The SESK method to determine optimum envelope analysis is presented. We have verified the potential of the SESK diagnostic methodology using real vibration data.