Leveraging predictive maintenance with machine learning and IoT for operational efficiency across industries

Nayak, Saugat (2025) Leveraging predictive maintenance with machine learning and IoT for operational efficiency across industries. International Journal of Science and Research Archive, 15 (1). pp. 1892-1910. ISSN 2582-8185

Based on the IoT and machine learning, predictive maintenance can be defined as the proactive approach toward equipment management, and it reshapes the traditional maintenance of assets. Contrary to corrective maintenance done once certain problems are experienced or proactive that occurs at planned intervals, proactive maintenance uses data collected to predict and avoid failures. Through IoT, companies receive real-time details of temperature, pressure, and vibration on their equipment and intervene at the right time. This is augmented by machine learning methods for analyzing volumes of data for anomaly detection as well as prognosis of the need for maintenance, thereby significantly minimizing cases of unscheduled downtimes and enhancing the life of the equipment. The benefits of predictive maintenance span multiple areas: Improved operation cost, improved production, safety, and environmental friendliness are some of the benefits obtained from the use of robotics. As compared to traditional maintenance practices, where decisions are made based on schedule, predictive maintenance reduces waste, conserves energy, and is environmentally friendly. In addition, the approach helps improve safety in the workplace since it focuses on identifying risks that may grow worse over time. However, the challenge arises when one seeks to introduce and adopt an effective form of predictive maintenance. Security is crucial since IoT networks carry and store large amounts of information that cannot be compromised. Scalability and the requirement for sophisticated data are also the major programmatic difficulties. However, edge computing, AI, and digital twins are anticipated to evolve and improve predictive maintenance in the future, making it an even faster, more accurate and sustainable solution. Overall, the smart approach of IoT and machine learning in predictive maintenance has already set new benchmarks in operational excellence and dependability. With the increasing sophistication of technology, this kind of preventive strategy will become a principal aspect of future industrial practices.