Computational Intelligence–Driven Health Monitoring Framework for Fault Diagnosis in Induction Machines

Abstract

downtime and ensure consistent performance, the development of an efficient health monitoring framework is crucial. This study introduces an innovative methodology for assessing the health condition of induction machines through computational intelligence techniques. The proposed framework incorporates advanced CI-based algorithms—combining machine learning models and signal-processing approaches—to interpret operational data and detect abnormalities or emerging faults.In this work, artificial neural networks, support vector machines, and genetic algorithms are employed to analyze sensor-derived data from the induction machine. Operating in real time, the system continuously evaluates critical parameters such as current, voltage, temperature, and vibration signatures, enabling the identification of deviations from normal operating behavior. By constructing baseline performance patterns and applying robust pattern-recognition techniques, the system can detect early indicators of component degradation or potential failures, supporting timely predictive maintenance actions. Additionally, the proposed health monitoring framework is designed to be adaptable and scalable, making it suitable for various induction machine types and diverse industrial environments. The integration of remote monitoring functionality further allows maintenance teams to access instant diagnostic insights and machine health information, thus enhancing decision-making efficiency and enabling proactive maintenance planning