Classification of Mechanical Faults in Rotating Machines Using SMOTE Method and Deep Neural Networks

Condition monitoring of electrical Rotating Machines (RM) serves in structural changes detection during machine’s operation. However, the frequent fault occurrence reduces the RM remaining useful life and accelerates their deterioration. Therefore, this paper proposes an effective multi-fault classification system for the faults in electric rotating machines. The proposed method employs an Artificial Neural Network (ANN) and Synthetic Minority Over-sampling (SMOTE) technique for automatically detecting rotating machines failures. This model's efficacy stems from the use of the relief feature selection approach to identify the most affecting features and improve the model's performance. A case study analysis uses the Machinery Fault Dataset (MAFAULDA) to test the models' performance. Simulation results are obtained to demonstrate that the proposed paradigm provides outstanding performance based on a fair assessment using the MAFAULDA dataset and shows that the proposed model has a high potential to detect rotating machine state.