New imbalanced fault diagnosis framework based on Cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data

Abstract Due to the complexity of their working conditions, historical rolling bearing datasets are mostly limited and imbalanced. The fault data may be composed of multiple subclusters; that is, the historical rolling bearing data have both between-class and within-class imbalances. While support vector machines (e.g., least squares support vector machines (LS-SVMs)) offer advantages when dealing with limited data, traditional fault diagnosis using an LS-SVM has the disadvantages of easy failure of complex imbalanced data and large dependence on the classifier hyperparameters. Therefore, this paper presents a new imbalanced fault diagnosis framework based on a cluster-majority weighted minority oversampling technique (Cluster-MWMOTE) and a moth-flame optimization (MFO)-based LS-SVM classifier. As an extension of MWMOTE, our proposed Cluster-MWMOTE combines the clustering algorithm represented by agglomerative hierarchical clustering (AHC) with MWMOTE. Unlike MWMOTE, Cluster-MWMOTE can avoid the ignoring of small subclusters of faulty (minority) instances far from normal (majority) instances. That is, Cluster-MWMOTE further improves the adaptation to within-class imbalances. As a novel heuristic intelligent algorithm, MFO exhibits faster convergence and higher precision than the traditional optimization algorithms (e.g., particle swarm optimization (PSO) and genetic algorithm (GA)). Therefore, we utilize MFO to optimize the hyperparameters (Sigma & γ ) of the LS-SVM classifier for the first time. The fault diagnosis results represented by CWRU and IMS bearing data suggest that the proposed framework provides higher fault diagnosis recognition rates and algorithm robustness than 16 existing algorithms.