A Survey on the Role of Big Data Analytics in Enhancing Predictive Maintenance for Smart Industries

Authors: Abhale B.A, Wadghule Y.M, Lakare Vishal R., Endait Sakshi D., Dhanwate Pallavi A., Kachole Manasi S.

Country: India

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Abstract: In modern industries, unexpected machine breakdowns can lead to significant production delays, financial losses, and increased maintenance costs. Traditional maintenance approaches, such as reactive maintenance or scheduled preventive maintenance, often fail to address these issues efficiently. Reactive maintenance results in unplanned downtime, while preventive maintenance may involve replacing components unnecessarily. To overcome these challenges, industries are increasingly adopting Predictive Maintenance, a proactive strategy that anticipates equipment failures before they occur, ensuring smoother operations and cost savings. The implementation of Predictive Maintenance relies heavily on the integration of Industrial Internet of Things (IIoT) devices and Big Data Analytics. Smart sensors installed on machines continuously collect real-time data on various parameters such as temperature, vibration, oil tank levels, and on/off status. This continuous monitoring allows for a comprehensive understanding of the operational condition of machinery. The large volume of data generated is processed using Big Data tools, which can handle, store, and analyze this information efficiently to extract meaningful insights about machine health and performance trends. [1]

Keywords: Predictive Maintenance Industrial Internet of Things (IIoT) Machine Learning (ML) Big Data Analytics Smart Sensors.

Paper Id: 232784

Published On: 2025-11-03

Published In: Volume 13, Issue 6, November-December 2025

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