Analysis of multiple faults in induction motor using machine learning techniques

Studying simultaneous faults in three-phase induction motors guarantees reliability, reduces unplanned downtime, and minimizes maintenance expenses in industrial settings. Induction motors have concurrent faults, including stator winding, rotor faults, unbalanced voltages, load fluctuations, and overvoltages. These make fault diagnosis difficult and can result in disastrous failures if not detected. Traditional diagnostic methods are expert judgment-based and pre-threshold-based and, therefore, less efficient when dealing with vast industrial processes. Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. Due to their limits, machine learning algorithms outperform traditional methods in real-time fault diagnosis, predictive maintenance, and multi-fault categorization. Through ensemble learning and feature selection, the models cope well with big data sets with enhanced fault classification accuracy and robustness against noise. In addition, ML fault analysis minimizes reliance on human experience and presents a computerized, scalable industrial motor condition monitoring method. Experimental outcomes show that excellent classification accuracy is achieved in ML models; hence, active maintenance and effective motor operation are feasible. The outcomes point to the potential of AI-based predictive maintenance to enhance safety, energy efficiency, and process continuity in industrial applications. Traditional fault detection methods rely on pre-established thresholds or expert-provided rules and may not perform effectively for concurrent multiple faults. In order to surpass these limitations, a new approach by using state-of-the-art machine learning algorithms such as Extreme Gradient Boosting (XGBoost) combined with Fuzzy Inference Systems (FIS) presents a new perspective towards improved accuracy and comprehensibility in fault detection. This method takes advantage of the ability of XGBoost to learn intricate relationships and utilizes FIS's rule-based reasoning for explainability. The incorporation of FIS in XGBoost enhances the precision of fault classification, handles uncertainty, and encourages interpretability.