Design and Implementation of Real Time Monitoring System of Diesel Generator using IoT

In an era dominated by technological advancements, the fusion of Internet of Things and Machine Learning has emerged as a transformative force, revolutionizing diverse industries. Among these, the realm of predictive maintenance stands out as a critical application, particularly in ensuring the reliability and efficiency of vital assets. This paper focuses on the integration of Machine Learning (ML) and the Internet of Things (IoT) to enhance predictive maintenance strategies for Diesel Generators (DG). Machine Learning Algorithms like, Random Forest and Support Vector Machines, are employed to predict potential faults and performance issues before they escalate. The project aims to provide a comprehensive solution for proactive maintenance, optimizing the efficiency and reliability of DG operations.. Data collected from sensors is processed and analyzed to derive valuable insights, enabling operators to make timely decisions regarding maintenance of Diesel Generator and system optimizations. This innovative approach not only minimizes downtime and reduces operational costs but also contributes to the overall evolution of smart and connected energy infrastructure. The paper signifies a critical step towards establishing a robust and efficient framework for predictive maintenance in the context of DG, aligning with the demands of contemporary energy needs and technological advancements.