Artificial Intelligence to detection fault on three phase squirrel cage induction motors subjected to broken bar fault

Abstract

Induction motors are widely used in various industrial applications due to their robustness, reliability, and low cost. However, they are also prone to various types of faults, such as broken rotor bars, bearing defects, stator winding faults, and eccentricity. These faults can cause performance degradation, energy loss, and even catastrophic failures if not detected and diagnosed in time. Therefore, condition monitoring and fault diagnosis of induction motors are essential for ensuring their safe and efficient operation. In this paper, we propose a novel fault diagnosis method for induction motors based on artificial intelligence, peak variation response (PVR), park vector approach (PVA), and standard deviation (SD). The proposed method consists of four steps: · Data acquisition and preprocessing, · Feature extraction using pvr and pva, · Feature selection using sd, and · Fault classification using artificial neural networks. The PVR and PVA are used to extract the amplitude and phase information of the stator current signals under different load conditions and fault types. The SD is used to select the most relevant features for fault diagnosis. The ANNs are used to classify the faults based on the selected features. The proposed method is validated by experimental results on a 1.5 kW three-phase induction motor with various simulated faults. The results show that the proposed method can effectively diagnose different types of faults with high accuracy and robustness.