Knowledge extraction using data mining for multi-class fault diagnosis of induction motor

• A novel but simple approach to remove redundant and irrelevant information, to extract the important knowledge (fault features) using data mining.
• Knowledge generated using CWT are redundant whereas that from HT are mostly irrelevant. Useful knowledge extracted using RST and GA.
• Performances of the data mining techniques have been judged by taking into account their applicability in two different types of dataset.
• Only radial (vertical) frame vibration is found sufficient for multi-class fault diagnosis of induction motor.

The feature extraction capability of rough set theory (RST) and genetic algorithm (GA) are used to extract knowledge from radial frame vibration signal for fault diagnosis of induction motor. This knowledge can assist in selecting scales for continuous wavelet transform (CWT) and mono-components required for Hilbert transform (HT) to extract fault related information from the vibration signal. Thus, the computational complexity of the signal processing tools is considerably reduced making both CWT and HT hardware friendly and suitable for real-time applications. For machine learning based automatic multi-class fault diagnosis, the performance of the classifiers are also considerably improved with significant reduction in computational burden since the redundant and irrelevant information can be effectively removed. The information obtained using data mining technique is successfully used to detect six types of induction motor faults. The results obtained are also verified in presence of high level of noise which has not been attempted earlier. The main contribution of the paper is to combine the advantages of two powerful signal processing tool like CWT and HT to extract hidden information from vibration signal in conjunction with data mining technique making them computationally efficient and easy to implement.