Ten lectures on wavelets
Expert system development for vibration analysis in machine condition monitoring
Expert Systems with Applications: An International Journal
Investigation of engine fault diagnosis using discrete wavelet transform and neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system
Expert Systems with Applications: An International Journal
Optimal selection of wavelet basis function applied to ECG signal denoising
Digital Signal Processing
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
An intelligent fault diagnosis system for newly assembled transmission
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper introduces an automatic feature extraction system for gear and bearing fault diagnosis using wavelet-based signal processing. Vibration signals recorded from two experimental set-ups were processed for gears and bearing conditions. Four statistical features were selected: standard deviation, variance, kurtosis, and fourth central moment of continuous wavelet coefficients of synchronized vibration signals (CWC-SVS). In this research, the mother wavelet selection is broadly discussed. 324 mother wavelet candidates were studied, and results show that Daubechies 44 (db44) has the most similar shape across both gear and bearing vibration signals. Next, an automatic feature extraction algorithm is introduced for gear and bearing defects. It also shows that the fourth central moment of CWC-SVS is a proper feature for both bearing and gear failure diagnosis. Standard deviation and variance of CWC-SVS demonstrated more appropriate outcome for bearings than gears. Kurtosis of CWC-SVS illustrated the acceptable performance for gears only. Results also show that although db44 is the most similar mother wavelet function across the vibration signals, it is not the proper function for all wavelet-based processing.