A Simulation of Non-stationary Signal Analysis Using Wavelet Transform Based on LabVIEW and Matlab

The condition monitoring of machines has long been accepted as a most effective solution in avoiding sudden shutdown and to detect and prevent failures in complex systems. Signal capture and analysis, and feature extraction and classification represent the main tasks in building any monitoring system. Signal processing plays a significant role in condition monitoring and the fault diagnosis process. Many types of signals can be used in the condition monitoring of machines, such as vibration, electrical and sound signals. Processing these signals in an appropriate way is crucial in extracting the most salient features related to specific types of faults. A variety of signal processing techniques can fulfil this purpose, and the nature of the captured signal is a significant factor in the selection of the appropriate technique. The main focus of this research is a consideration of signal processing techniques which can be applied in condition monitoring, and to identify their advantages and disadvantages. Then, the wavelet transform is discussed in detail. After that, a monitoring system based on multi-resolution analysis using the wavelet transform is successfully simulated using LabVIEW and Mat lab capabilities. The results show that the differences between healthy and faulty signals can be effectively detected using the wavelet transform.