Bearing Fault Detection For Water Pumping System Using Artificial Neural Network

Bearing fault is a frequently occurring problem. In induction, motor and vibrational results can give data on the machine's health condition. The collection of vibrational signals through an external sensor is very costly. Conventional methods are time-consuming and expensive. Some of them are less efficient and require some energy to choose the signal. To overcome the problems machine learning (ML) algorithm-based model can determine the failure at an early stage. This work develops the Matlab Simulink model for the identification of bearing fault in induction motor-based pumping systems. In the experiment, the vibrational data have been collected by losing the bearing to some extent. The vibrational data have been applied in the Simulink model for verification of the system failure. Where ML-based artificial neural network (ANN) combined with genetic algorithm (GA), other ML algorithms like K-nearest neighbor (KNN), support vector machine (SVM), and decision tree is applied and compared based on evaluation parameters like precision, F1 score, and sensitivity to find out the best-suited algorithm. The result shows that the accuracy of ANN is more than other ML algorithms.