Fan fault diagnosis based on wavelet spectral analysis

Fan plays an irreplaceable role in the ventilation of underground environment. It will cause serious consequences once the fan fails. So it is essential to do fault diagnosis of fan to prevent the emergence of break down. Spectral analysis method is a simple and effective method for signal analysis and it has been widely used in fault diagnosis of rotating machinery. This paper has researched the bearing fault of fan on the base of in-depth study of the frequency characteristics of typical faults fan. High-frequency noise of vibration signal has been removed through wavelet soft thresholding method. Get its spectrum characteristic of the reconstructed signal, and then the failure category of signal is discriminated by comparing its characteristic frequency to typical faults frequency in theory. Combining the advantage of LabVIEW and MATLAB, we implement the algorithm through two of them, accomplishing denoising and spectral analysis of vibration signals, completing fault diagnosis of fan.