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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: December 2025
Volume 10 | Issue 12
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19445
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 144
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Published Paper Details
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| Paper Title: | Data-Driven Approach for OEE Enhancement in the Manufacturing Industry |
| Authors Name: | Shravani Shalgar , Tanmay Landge , Prerana Jagtap |
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IJRTI_200322
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| Published Paper Id: | IJRTI2501041 |
| Published In: | Volume 10 Issue 1, January-2025 |
| DOI: | |
| Abstract: | This paper proposes a predictive maintenance system designed for industrial machinery, integrating IoT technology, cloud computing, and machine learning. The system uses IoT-enabled ESP32 microcontrollers to collect real-time operational data such as positional deviations, production counts, and cycle time. Machines without built-in IoT capabilities are retrofitted with sensors to ensure uniform monitoring. Collected data is transmitted securely to an OpenStack-based cloud infrastructure via an API gateway, enabling scalable data storage and processing. The preprocessing pipeline cleans and normalizes the data, ensuring it is suitable for advanced predictive analytics. The Auto-Regressive Integrated Moving Average (ARIMA) model is employed for time-series forecasting to detect anomalies and predict machine failures. These predictions, along with real-time machine metrics are visualized on a web-based dashboard. Operators can monitor key performance indicators (KPIs) such as production, quality, and machine health, facilitating informed decision-making and minimizing unplanned downtime. Feedback loops enhance the model's accuracy and system performance over time. The proposed architecture demonstrates scalability, robustness, and effectiveness in reducing operational inefficiencies and increasing equipment lifespan. By combining IoT and cloud-based predictive analytics, the system contributes to Industry 4.0's vision of smart, interconnected factories. Experimental results confirm the feasibility and practicality of the system in industrial applications |
| Keywords: | IoT, Predictive Maintenance, Industrial Automation, Machine Learning. |
| Cite Article: | "Data-Driven Approach for OEE Enhancement in the Manufacturing Industry", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 1, page no.a311-a316, January-2025, Available :http://www.ijrti.org/papers/IJRTI2501041.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2501041
Registration ID:200322
Published In: Volume 10 Issue 1, January-2025
DOI (Digital Object Identifier):
Page No: a311-a316 Country: Pune, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2501041 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2501041 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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