Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study
This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data direct...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/537 |
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| author | Riccardo Mennilli Luigi Mazza Andrea Mura |
| author_facet | Riccardo Mennilli Luigi Mazza Andrea Mura |
| author_sort | Riccardo Mennilli |
| collection | DOAJ |
| description | This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments. |
| format | Article |
| id | doaj-art-37a738048f9245be91695d8fde8b22c6 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-37a738048f9245be91695d8fde8b22c62025-01-24T13:49:16ZengMDPI AGSensors1424-82202025-01-0125253710.3390/s25020537Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility StudyRiccardo Mennilli0Luigi Mazza1Andrea Mura2Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyThis study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments.https://www.mdpi.com/1424-8220/25/2/537PLCArduino boardindustrial automationedge computingmachine learningpredictive maintenance |
| spellingShingle | Riccardo Mennilli Luigi Mazza Andrea Mura Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study Sensors PLC Arduino board industrial automation edge computing machine learning predictive maintenance |
| title | Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study |
| title_full | Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study |
| title_fullStr | Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study |
| title_full_unstemmed | Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study |
| title_short | Integrating Machine Learning for Predictive Maintenance on Resource-Constrained PLCs: A Feasibility Study |
| title_sort | integrating machine learning for predictive maintenance on resource constrained plcs a feasibility study |
| topic | PLC Arduino board industrial automation edge computing machine learning predictive maintenance |
| url | https://www.mdpi.com/1424-8220/25/2/537 |
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