Machinery fault diagnosis using advanced correlation filters - Loo, C. K.; Mastorakis, Nikos E. (2007)
The purpose of machine condition monitoring is to determine the present health of machineries. Capturing the abnormal symptoms of machineries from vibration signatures involves the use of signal processing algorithms on measured vibrations. However, the commonly used method such as FFT based power spectra assumes the the signal is ergodic and stationary. The FFT based method may produce unpredictable results especially in an industrial environment that subjected to random or periodic noise. Such effects can be detected, however, with second-order cyclostationary statistical method such as Degree of Cyclostationary (DCS). This paper discussed the implementation of machinery fault diagnosis using the Quad-Phase Unconstrained Optimal Tradeoff Synthetic Discriminant Function (QUOTSDF) and DCS features for fault classification. Quad-Phase Unconstrained Optimal Tradeoff Synthetic Discriminant Function (QUOTSDF) is used in this effort because of its ability to provide high discrimination while providing noise tolerance. The machine health condition is identified based on the comparison of acquired real-time vibration features with template features. The vibration data were collected from the Schenck Motor MM-61. Four machinery conditions are simulated by the motor, which are normal (no fault), bearing damage, machine imbalance, and foundation looseness. The Fast Fourier Transform (FFT) and the Degree of Cyclostationary (DCS) have been utilized for features extraction from the power spectrum of the vibration data. Fault diagnosis based on DCS features are shown to outperform FFT in accuracy.