Machine Learning-Based Predictive Maintenance Framework for Performance Degradation Detection in Energy Conversion Systems

Energy conversion systems, such as refrigeration units, electric motors, compressors, and thermal management systems, are very important in the use of energy in both industry and ordinary households. Performance degradation and component failure of energy conversion systems lead to both unexpected equipment downtime and a significant loss of energy efficiency. The typical method of maintaining these systems is either reactive or scheduled maintenance, and does not provide the tools for early detection of the degradation. In this paper, we will present an artificial intelligence-based predictive maintenance framework designed for monitoring and diagnosing Energy Conversion Systems (ECSs) using multiple sensor-generated operational data. The proposed methodology integrates a variety of parameters, including temperature, vibration, current, and environmental parameters, to develop a model of the health of ECS operating under normal conditions. Machine learning is then applied to the obtained data to identify anomalies, identify early fault signatures, and predict likely future failures prior to a major breakdown. The framework helps achieve both fault prevention and optimising energy efficiency through correlating equipment health indicators with energy performance metrics. Through experimental analyses of predictive maintenance, the findings show that not only does predictive maintenance reduce unplanned downtime, but it also exhibits verifiable improvements in system efficiency and operational stability. According to the study, the use of machine learning algorithms in predictive maintenance provides a robust framework for improving the reliability and energy efficiency of energy conversion systems. A framework that continuously learns from multi-sensor operational data allows for the identification and thus maintenance of both the early stages of performance degradation and early identification of potential failure modes. The framework's combination of health indicators and energy performance metrics makes it possible to provide a quantitative evaluation of the total amount of efficiency loss due to deterioration in energy conversion systems. With these results, it is possible to see the potential for AI-based maintenance to facilitate sustainable energy management, decrease operational losses, and build the resiliency of systems in today's smart and connected energy conversion systems.