Abstract:

The advent of Industry 4.0 has transformed manufacturing processes globally, with predictive maintenance emerging as a critical enabler for efficiency, cost reduction, and equipment longevity. In India’s fast-evolving manufacturing sector, unplanned equipment downtime continues to hinder productivity and competitiveness. This paper proposes an IoT-enabled predictive maintenance framework tailored to the needs of Indian manufacturing systems. The study integrates real-time sensor data acquisition, machine learning-based fault prediction, and cloud-based analytics to enable proactive decision-making. Using vibration analysis, temperature monitoring, and energy consumption patterns, the system can detect early warning signs of equipment degradation, thereby reducing maintenance costs and enhancing operational reliability. The proposed framework emphasizes affordability, scalability, and compatibility with legacy systems, making it viable for small and medium-sized manufacturing enterprises in India. This research contributes to bridging the technological gap in industrial maintenance practices, facilitating the transition toward smart manufacturing in line with the “Make in India” initiative. 

Keywords: Predictive Maintenance, IoT, Industry 4.0, Smart Manufacturing, Machine Learning, Indian Industry