ANALYZING PERFORMANCE OF SMART PREDICTIVE MAINTENANCE SYSTEMS IN INDIAN FARMING AUTOMOBILE THROUGH EXPERIMENTATION

Abstract

Agricultural sector in India is highly significant to the economy of the nation and transport and machineries systems that facilitate the sector are necessary in ensuring the farming is efficient and productive. However, upkeep issues usually lead to unexpected downtimes, which negatively affect the capacity of farms to earn and to be productive. This study aims at investigating the effectiveness of intelligent predictive repair systems in the field vehicles in Indian agricultural conditions. It is a controlled test that is conducted in a sequence of experiments to investigate the effectiveness of predictive maintenance techniques in identifying issues prior to their occurrence and ensuring timely repairs. The sensors, IoT devices, and machine learning methods are used in this approach to monitor the health of farm machinery such as a tractor, plough, and harvester. In the beginning of the work, the Internet of Things (IoT) sensors are installed on various farming vehicles to gather real-time data about their work, including temperature, shaking, and engine performance. The information is forwarded to a cloud-based solution and analysed. Machine learning algorithms, including regression and classification models, are used to predictive models that establish what can go wrong and what maintenance is to be done. The experiment design evaluates the accuracy, reliability, and performance of these smart predictive maintenance systems in reducing the downtime and utilizing work plans optimally. Besides this, the paper also examines how these systems can be expanded and whether it can be engaged in rural areas with limited technology. According to the results, predictive maintenance systems can reduce the unexpected downtimes and repair costs significantly. This will make work more effective and enhance the overall performance of farm machines.