Abstract
The diagnosis of hydraulic pump health in realtime is important for increasing hydraulic system reliability andperformance. Because of high noise levels in the pump pulsation pressure signal, many existing health diagnosis methods,such as limit checking, spectrum analysis, and logic reasoning, cannot effectively perform a reliable online health diagnosisfor hydraulic pumps. Wavelet analysis, a waveform signal analysis method performed by breaking up an evaluating signalinto shifted and scaled versions of a standard wavelet, can identify feature signals in multiple decomposed band windows ofthe original signal. The methodology for applying this wavelet analysis in realtime health diagnosis for hydraulic pumpswas investigated in this study. Results obtained from both simulation analysis and online experimental validation verifiedthat the wavelet analysis method can improve the capability of diagnosing the health conditions of piston pumps, and moreimportantly can identify the types of pump defects based on the patterns and the amplitudes of obtained wavelet coefficients.
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