A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA

A Novel Fault Diagnosis of Induction Motor by Using Various Soft Computation Techniques: BESO-RDFA

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This paper presents a hybrid prediction technique for fault detection of induction machines.The established hybrid forecast scheme signifies the combined execution of Bald-Eagle- Search-Optimization (BESO) and Random-Decision-Forest-Algorithm (RDFA), called as BESO-RDFA prediction scheme.This proposed technique is used to predict the fault within a short period in the rotating machines.By considering 5x4x3 box the machine defects the RDFA is trained by using the BESO-based exact prediction with data in online mode.The MATLAB/Simulink work platform is employed to execute the model, which is then assessed using multiple techniques to forecast attributes and models of impending stator failure.

A new robust diagnostic design is established to analyze the incipient stator winding blue square tablecloth failures.Simulation analysis shows the detection and isolation method with great sensitivity indicating the incipient winding failures.