Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively.