Energy Conservation of Electrical Machines in Local Plant Factory Using Preventive Maintenancene and State Estimation

This paper investigates the application of predictive maintenance strategies, state estimation techniques, and the cumulative maintenance cost function to enhance energy conservation in electrical machines within a local plant factory in Nakhon Phanom province, Thailand. The research methodology encompasses a comprehensive site survey, problem analysis, and the development and implementation of targeted solutions. Focusing on motor fault detection, the study employs current and vibration signal analysis to monitor machine health. Results indicate that excessive vibration levels frequently lead to ball bearing damage and subsequent electrical malfunctions. By implementing vibration monitoring and utilizing the cumulative maintenance cost function, motor degradation can be effectively anticipated, allowing for preventive measures that improve reliability and optimize maintenance costs over time. This predictive maintenance approach has practical applications in industrial environments, offering potential improvements in energy efficiency, extending equipment lifespan, and managing long-term maintenance expenses.