Gearbox health monitoring experiment using uibration signals

Machine deterioration prognosis is one of the most important considerations to reduce the cost of maintenance. This experiment demonstrates the processing of ribration signals for gear fault detection. An artificial defect teas introduced to the gearbox as a full tooth breakage (missing tooth). Vibration signals were collected for run-up operation and at some fixed speeds. The kurtosis statisticeal parameter teas used as a measure of impact sererily in the time domain of the ribndion signal. The fast Fourier transform (FFT) and the wavelet analysis (two widely applied techniques for machine health monitoring) were also used for spur gear fault detection. The kurtosis was found to be higher for the case of faulty gears. Also, the experiment demonstrated that both FFT and warelet techniques were very sensitive to gear faulls and imperfections.