This paper introduces an efficient and powerful approach to fault detection in rotating machinery using time-frequency analysis based on the wavelet transform of the monitored shaft vibration signal. Wavelet techniques are one of the latest powerful tools in analyzing the transient information for condition monitoring and fault detection using vibration signature. The proposed technique combines both the Continuous Wavelet and the Wavelet Packet Transforms. In particular, it exploits the use of the modulus of the local maxima lines in the wavelet domain, to detect impulsive mechanical faults through shaft vibration such as impact blade-to-stator rubbing in turbo machinery.
The proposed new wavelet-based signal processing method was able to detect the singularity in the measured shaft vibration, which was generated by blade rubbing. The singularity detection achieved by the new method was very well supported by its counterpart based on the direct blade vibration measurements.
Our proposed technique was favorably compared with both the time wave and the traditional Fourier Transform techniques. In fact, both the analysis and the extensive simulation work show the superiority of the combined approach (Wavelet Packet Transform and Maxima Lines) over the traditional Fourier-based method, in reliably diagnosing impulsive mechanical faults.