Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

Summary A novel framework is proposed for the classification of motor bearing faults by using multiwavelet transform and naive Bayes classifier. This work has explored the application of multiwavelet transform to the vibration signatures for extracting the effective fault features. Geronimo‐Hardin‐Massopust multiwavelet filter bank has been employed in this work for multiresolution analysis up to fourth decomposition level. This work has relied upon multiwavelets as the multiwavelets are enriched with the properties which wavelets do not offer simultaneously like compact support and orthogonality without compromising with symmetricity and higher order of vanishing moments. After that, fault features have been extracted from the decomposed vibration signals to utilize the fault information present at different resolution levels. The fault classification has been accomplished with high success rate by using the extracted fault features with the naive Bayes classifier due to its inherent features like robustness to noise, simplicity, and quick convergence even with less training data. The efficacy of the proposed classifier has been verified with both the offline signals and the real‐time signals having different fault severity levels. The outperformance of the proposed classifier has been shown by comparing the classification results with other established supervised classification techniques like probabilistic neural network, Gaussian‐support vector machine, polynomial‐support vector machine, and linear‐support vector machine.