Udai Chandra Jha, R. Jitendra Sai, M. V. B. Krishna Reddy, Abhishek Singh



Analysis of Predictive Maintenance in Industry 4.0: A review

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Any unplanned downtime of industrial equipment or systems may degrade or disrupt a company's core operation, resulting in substantial fines and long-term brand harm. Traditional maintenance methods have certain assumptions and limitations, such as high repair costs, overtime costs, mathematical deterioration processes that are inefficient and manual function extraction. Smart manufacturing, as well as advances in the Internet of Things (IoT), machine learning (ML), artificial intelligence (AI), and advanced powerful and inexpensive sensors, are leading to Industry 4.0's predictive maintenance (PdM). It is possible to collect massive amounts of operational and process condition data produced by several pieces of equipment and harvest data for automated fault detection and diagnosis as a result of the digital transformation towards industry 4.0, information techniques, computerized control, and communication networks, with the goal of minimizing downtime and the utilization rate of the component. Predictive maintenance allows industry to intervene before harm happens, saving both time and money. PdM is unavoidable in Industry 4.0 for long-term smart manufacturing. In this project, we focused on the study of equipment, predictive maintenance methods and implementations, as well as the creation of a model for a modern predictive maintenance system in Industry 4.0 and a market analysis of predictive maintenance.


Predictive maintenance, Industry 4.0, artificial intelligence, Machine learning, Industrial IoT, COVID-19, PdM market


Cite this paper

Udai Chandra Jha, R. Jitendra Sai, M. V. B. Krishna Reddy, Abhishek Singh. (2022) Analysis of Predictive Maintenance in Industry 4.0: A review. International Journal of Mechanical Engineering, 7, 103-109


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