Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor

Induction motors are subject to different faults which, if undetected, may lead to serious machine failures. From the scrupulous review of related works, it is observed that neuro-fuzzy and neural network (NN)-based fault-detection schemes are performed well for large machines and they are not only expensive but also complex. In this paper, the authors developed the radial-basis-function-multilayer-perceptron cascade-connection NN-based fault-detection scheme for the small and medium sizes of three-phase induction motors. Stator winding interturn short, rotor eccentricity, and both faults simultaneously are selected for demonstration. Simple statistical parameters of stator current are considered as input features. Principal component analysis is used to select suitable inputs to the network. Experimental results are included to show the ability of the proposed classifier for detecting faults. Moreover, the network is tested for the robustness to the uniform and Gaussian noises. Having good classification accuracy with enough robustness to noises, the proposed classifier is suitable for the real-world applications.