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Unbalance fault localization in rotating machinery disks using EEMD and optimized multi-class SVM

This study aimed to discover the locations of the unbalance faults on rotating machine discs. In this regard, fair comparisons between the performances of empirical mode decomposition, ensemble empirical mode decomposition and discrete wavelet transform were performed to determine unbalance fault location in a rotating machinery. In order to obtain the required data, a rotating machinery fault simulator with three discs was designed and made for investigating unbalance faults effects. The fault classification process was done using a multi-class support vector machine optimized by particle swarm optimization algorithm. The obtained results indicated that ensemble empirical mode decomposition method had higher success rate in locating unbalance fault compared to other methods. In addition, one-against-one approach by the optimized multi-class support vector machines outperforms one-against-all approach.