Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing model

•Perform offline as well as online fault detection and diagnosis of induction motors.•Extract current harmonic patterns with the Motor Current Signature Analysis method.•Deploy the Fuzzy Min-Max (FMM) neural network to learn fault patterns incrementally.•Use the Classification and Regression Tree (CART) to refine the knowledge from FMM.•Monitor multiple motors and provide predicted fault conditions to users (engineers).

In this paper, a hybrid soft computing model comprising the Fuzzy Min-Max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis is described. Specifically, the hybrid model, known as FMM-CART, is used to detect and classify fault conditions of induction motors in both offline and online environments. A series of experiments is conducted, whereby the Motor Current Signature Analysis (MCSA) method is applied to form a database containing stator current signatures under different motor conditions. The signal harmonics from the power spectral density (PSD) are extracted, and used as the discriminative input features for fault classification with FMM-CART. Three main induction motor conditions, viz. broken rotor bars, stator winding faults, and unbalanced supply, are used to evaluate the effectiveness of FMM-CART. The results indicate that FMM-CART is able to detect motor faults in the early stage, in order to avoid further damage to the induction motor as well as the overall machine or system that uses the motor in its operations.

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