This work focuses on the application of the well-known signal processing techniques such as the time series models, Fourier transform, and wavelet transform in visualizing peaks of vibration and their pattern that are used in structural health monitoring. The primary objective of this study is to compare the ability of the continuous wavelet transform (CWT) series and the Fast Fourier Transform (FFT) series in detecting mechanical faults, specifically looseness and bearing condition, in an electrical motor simulator through the visualization of vibration peak changes. By utilizing these two signal processing techniques, the frequency peaks caused by alterations in the structure have been compared. It is done on a vibration experiment under different bearing conditions such as normal condition, looseness of bearing mountings at the mid of the shaft and loose end condition, bearing damage at mid and end condition. These defects are performed using two different speeds. The vibrations were measured with a Dytran Triaxial Accelerometer with three different axis which were X, Y and Z axis. Then, the raw data obtained in acceleration transformed into time series, Fourier transform and finally wavelet transform using Matlab software. As the raw data was collected in time series, they are transformed to frequency spectrum using the Fourier transform. The frequency data have been chosen by the comparison of the X, Y and Z axis in time series based on the most significant amplitudes in respective to the three-axis stated. Finally, continuous wavelet transform (CWT) series are compared with the frequency peaks obtained using the Fast Fourier Transform (FFT). CWT used to plot the data by using magnitude scalogram method. It is shown that this method has provided a better way to visualize and identify the vibration peaks through all frequency ranges with respect to time and magnitude of vibration. One notable advantage of employing CWT is the simultaneous display of magnitude and time measurements alongside color-scaled frequency peaks on the plot. This scalogram visualization permits more precise detection of the fluctuation of vibration peaks than the FFT, which can be laborious. Therefore, CWT has the better effective techniques in detection of high vibration in scope of this work.
