Intelligent Fault Diagnosis in Electric Motors Using AI Techniques

Abstract

Many industrial uses depend on electric motors to work reliably, which is important for keeping things running smoothly and reducing downtime. On the other hand, these motors can have a number of problems that can make them work less well or even fail completely. Traditional ways of finding faults often rely on visual inspections or simple rule-based programs, which aren't always accurate or efficient. The idea in this study is to use advanced artificial intelligence (AI) methods to create a smart system for diagnosing problems in electric motors. Adding AI could help make fault finding, classification, and prediction more automated, which would improve the performance and efficiency of motor systems. The suggested approach uses machine learning techniques like deep learning, support vector machines (SVM), and ensemble methods to look at motion data and find trends that can help find problems. The first important step in the suggested scheme is data gathering, which involves getting different sensor readings from the motor system. Then, these data are preprocessed to get rid of noise and information that isn't important. This makes sure that the input data for the next study is of high quality. Feature extraction methods are used to get unique information from the motor data, which makes fault differentiation easier. The clever fault detection module is the most important part of the framework. It trains AI models to correctly identify different types of faults by using tagged data. Deep learning designs, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are better at understanding complex fault patterns from raw sensing data. By mixing multiple base learning, ensemble methods like random forests and gradient boosting also improve the accuracy of classification.