الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 200 |  |  |
| | | from | 200 | | |
Abstract
Machinery condition monitoring based upon vibration analysis techniques has generally relied on Fourier-based analysis as a method for translating vibration signals in time domain to the frequency domain. This thesis employs the application of the wavelet transform (WT)to both stationary and non-stationary vibration signals to investigate its sensitivity and limitation in detecting different faults. The detection and diagnostic capability of the wavelet analysis is compared to the Fourier-based analysis on the basis of the experimental results. The faults are artificially created in a special test rig built for this purpose. The defects included chipped and broken gear teeth, gear assembly faults, and bearings as examples of non-stationary signals, and rotor unbalance as an example of stationary signals. The sensitivity to fault severity to fault severity, and to transducer orientation will be investigated For gear and bearing faults, adaptive Morlet wavelet filters are developed based upon the Kurtosis maximization principle. The parameter β and scale a controlling the wavelet filter are varied to match with the nature of the fault feature to be extracted from the different signals. Also, several simulated examples are employed to demonstrate the advantages of the wavelet transform over the fast Fourier transform (FFT) and the short-time Fourier transform (STFT).
The experimental results considered in this thesis, show that the WT is very effective in the detection of both local and distributed gear faults. Both WT and envelope analysis are suitable for the detection of the different bearing faults employed. But envelope analysis is much easier to implement in industry. The FFT analysis is much suitable for unbalance faults detection than the WT that fails completely to diagnose rotor unbalance.