Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

• Two powerful signal processing tools CWT and HT used for feature extraction.
• A novel approach to find the important fault frequencies using GA.
• Seven important fault frequencies found to exist in frequency range 50–300 Hz. A new method of selecting CWT scales according to the fault frequencies found.
• Only vertical frame vibration found sufficient for multi-class fault diagnosis.
• Six induction motor faults considered and successfully detected.

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT + GA and HT + GA for radial vibration signal.

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