Early Detection of Defects in Gear Systems Using Autocorrelation of Morlet Wavelet Transforms

Abstract

The supervision task of industrial systems is vital, and the prediction of damage avoids many problems. If any system defects are not detected in the early stage, this system will continue to degrade, which may cause serious economic loss. In industrial systems, the defects change the behaviour and characteristics of the vibration signal. This change is the signature of the presence of the defect. The challenge is the early detection of this signature. The difficulty of the vibration signal is that the signal is very noisy, non-stationary and non-linear. In this study, a new method for the early defect detection of a gear system is proposed. This approach is based on vibration analysis by finding the defect’s signature in the vibration signal. This approach has used the autocorrelation of Morlet wavelet transforms (AMWT). Firstly, simulation validation is introduced. The validation of the approach on a real system is given in the second validation part.

Improvements in the Wavelet Transform and Its Variations: Concepts and Applications in Diagnosing Gearbox in Non-Stationary Conditions

“…Ohue [51] and colleagues analyzed gearbox vibration using DWT and CWT to investigate the correlation between the wavelet coefficient and the distribution of faults on the gear. Ayad et al, applied CWT and DWT with optimized parameters combined with autocorrelation functions to diagnose faults in industrial gearboxes [52].…”

“…Researchers also employed wavelet analysis as an efficient preprocessing method to enhance the quality of diagnostic information in signals and reduce noise. Al-Raheem and Ayad M. et al, diagnosed the rolling bearing fault in the gearbox using the noise-reduced signal's autocorrelation function, which was processed with CWT [53,54]. Jafarizadeh and colleagues have combined the noise removal method through time-domain averaging with the Morlet wavelet "filter" to diagnose tooth faults in gears from measured raw signals [55].…”