Abstract
Gearbox and rolling element bearing vibration signals feature modulation, thus being cyclostationary. Therefore, the cyclic correlation and cyclic spectrum are suited to analyze their modulation characteristics and thereby extract gearbox and bearing fault symptoms. In order to thoroughly understand the cyclostationarity of gearbox and bearing vibrations, the explicit expressions of cyclic correlation and cyclic spectrum for amplitude modulation and frequency modulation (AM-FM) signals are derived, and their properties are summarized. The theoretical derivations are illustrated and validated by gearbox and bearing experimental signal analyses. The modulation characteristics caused by gearbox and bearing faults are extracted. In faulty gearbox and bearing cases, more peaks appear in cyclic correlation slice of 0 lag and cyclic spectrum, than in healthy cases. The gear and bearing faults are detected by checking the presence or monitoring the magnitude change of peaks in cyclic correlation and cyclic spectrum and are located according to the peak cyclic frequency locations or sideband frequency spacing.
Highlights
Gearboxes and rolling element bearings are critical mechanical components and widely used in many types of machinery [1,2,3]
Feng and his collaborators [22,23,24] derived the expressions of cyclic correlation and cyclic spectrum for gear amplitude modulation (AM)-frequency modulation (FM) vibration signals and proposed indicators based on cyclic correlation and cyclic spectrum for detection and assessment of gearbox fault
Since gearbox and rolling element bearing vibration signals are characterized by AM-FM feature, their Fourier spectra have complex sideband structure due to the convolution between the Fourier spectra of AM and FM parts as well as the infinite Bessel series expansion of an FM term
Summary
Gearboxes and rolling element bearings are critical mechanical components and widely used in many types of machinery [1,2,3]. Shock and Vibration an equivalent cyclic energy indicator for rolling element bearing degradation evaluation These researches illustrate the effectiveness of cyclostationary analysis in gearbox and bearing fault diagnosis. Cyclic correlation and cyclic spectrum are effective in extracting modulation features from amplitude modulation (AM), frequency modulation (FM), and AM-FM signals Feng and his collaborators [22,23,24] derived the expressions of cyclic correlation and cyclic spectrum for gear AM-FM vibration signals and proposed indicators based on cyclic correlation and cyclic spectrum for detection and assessment of gearbox fault. We derive the explicit expressions of cyclic correlation and cyclic spectrum for general AM-FM signals, summarize their properties, and further extend the theoretical derivations to modulation analysis of both gear and rolling element bearing vibration signals, enabling cyclostationary analysis to detect and locate both gearbox and bearing fault
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
