Wavelet transform-based signal waveform discrimination for inspection of rotating machinery

This paper presents a new discrimination procedure of signal waveforms based on wavelet theory for the inspection of rotating machinery. The wavelet transform decomposes signals into time-frequency space, rather than mere frequency space, limited by the uncertainty principle. This decomposition permits time-frequency analyses and provides a more flexible means of signal processing than before. To examine a rotary compressor pump, particular waves in the rotational load torque signals that correlate with failure modes are discriminated from one another and evaluated. To extract the focal waves, the signal is decomposed with wavelets and then only particular waves, such as impulses, are reconstructed from a selected set of wavelet coefficients. This is called time frequency space filtering. The wavelet local modulus maxima are used to pen a time-frequency window through which only the focal waves can pass with high fidelity. The maxima have information of the inflection points of the wave at each resolution that represent its waveform. The experimental results show the effectiveness of the procedure.