Analysis of machinery faults by curve length and wavelet transforms

Rotation is a basic motion which is widely used in machinery and equipments of industry and energy production sites. The continuity of the motion is very important necessity. An unpredictable fault causing a stop or decrease of the performance in the system cause serious financial losses. For that reason, the necessity of predicting the fault arises. Considering the rotating machinery it can be concluded that the system basically consists of a shaft, housings and rolling element bearings. The basic faults are basically run-out, unbalanced masses and rolling element faults in such a kind of rotating machinery. The prediction of these faults and taking the corresponding precautions before the failure causes big financial savings. The most important method for this purpose is condition monitoring. Vibration measurements are mainly and widely used tool for condition monitoring.With the help of this point of view, the corresponding studies are worked on an experimental setup which can simulate the situation in the real life applications. The common faults such as run-out, unbalance and inner race defect cases were configured on the system and the condition was monitored by using vibration data. Run-out fault was performed with the help of movable housings, unbalance fault was created with the help of a circular plate, which has holes in the radial direction for mass fixing and roller bearing fault (especially inner race fault) was created with the help of electrical discharge machine (creating defects on the outer surface of inner race of bearing). Vibration measurements were performed with a portable vibration analyzer at a wide range of shaft speeds. Velocity and acceleration data were recorded. Vibration signals which were taken from healthy and faulty system were investigated in time domain by using statistical parameters such as rms, kurtosis and peak to peak. In the further step, curve length transform, which is a nonlinear time domain transform, was applied to vibration signals and again healthy and faulty system, were investigated in time domain by using statistical parameters such as rms, kurtosis and peak to peak. In addition to this process, effect of scale factor on curve length transform was examined. In the next step, fast Fourier transform (FFT) and short time Fourier transform (STFT) were applied on the vibration signals and frequency spectrum was investigated with aiming to get the characteristic fault frequencies. In the final step, continuous wavelet transform was applied to vibration signals and corresponding spectrums were created for giving more information about the fault frequency, fault time and fault amplitude.