Support vector machine based decision for mechanical fault condition monitoring in induction motor using an advanced Hilbert-Park transform

In this work we suggest an original fault signature based on an improved combination of Hilbert and Park transforms. Starting from this combination we can create two fault signatures: Hilbert modulus current space vector (HMCSV) and Hilbert phase current space vector (HPCSV). These two fault signatures are subsequently analysed using the classical fast Fourier transform (FFT). The effects of mechanical faults on the HMCSV and HPCSV spectrums are described, and the related frequencies are determined. The magnitudes of spectral components, relative to the studied faults (air-gap eccentricity and outer raceway ball bearing defect), are extracted in order to develop the input vector necessary for learning and testing the support vector machine with an aim of classifying automatically the various states of the induction motor.

► Original fault signature using an improved combination of Hilbert and Park transforms is proposed. ► The proposed fault signature shows its effectiveness and its robustness in mechanical fault detection. ► Support vector machine approach is used in order to classify automatically the various machine conditions.