Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning

Sensors, Journal Year: 2023, Volume and Issue: 23(19), P. 8286 - 8286

Published: Oct. 7, 2023

In an increasingly technology-driven world, the security of Internet-of-Things systems has become a top priority. This article presents study on implementation solutions in innovative manufacturing plant using IoT and machine learning. The research was based collecting historical data from telemetry sensors, cameras, control devices smart plant. provided basis for training learning models, which were used real-time anomaly detection. After we achieved 13% improvement detection rate 3% decrease false positive rate. These results significantly impacted efficiency safety, with faster more effective responses seen to unusual events. showed that there significant impact safety Improved enabled events, decreasing critical incidents improving overall security. Additionally, algorithm optimization infrastructure improved operational by reducing unscheduled downtime increasing resource utilization. highlights effectiveness learning-based comparing those previous industrial environments. adaptability these makes them applicable various commercial

Language: Английский

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Intelligent System for Conformity Assessment Testing on the Energy Performance of Household Appliances—A South African Perspective

Springer proceedings in earth and environmental sciences, Journal Year: 2025, Volume and Issue: unknown, P. 226 - 237

Published: Jan. 1, 2025

Language: Английский

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Analyzing the Transferability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to the Field of Oil and Gas Research

Published: Oct. 17, 2023

Anomaly detection in equipment processes plays an important role the oil and gas sector. Algorithms for detecting anomalies measured data are best understood computer science mathematics. Therefore, a possible transfer of knowledge from latter area to former can play significant role. This paper addresses such task by analyzing bibliometric Computer Science Mathematics papers published MDPI journals publications found on SPE search platform. It is shown that main algorithms both extensively studied reflecting anomaly problem Random Forest, Support Vector Machine, Long-term Memory Method Recurrent Neural Network. The advantages disadvantages these methods briefly described. Examples classical, highly cited describing work given. their application industry sections disciplines with largest number using above frequently used presented.

Language: Английский

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Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research

Energy Systems Research, Journal Year: 2024, Volume and Issue: 7(1(25)), P. 17 - 30

Published: April 30, 2024

Anomaly detection in equipment processes is crucial for the oil and gas sector. Algorithms detecting anomalies measured data are best understood Computer Science Mathematics. Therefore, a possible transfer of knowledge from latter area to former can have profound impact. This paper explores potential by analyzing bibliometric Mathematics papers published MDPI journals, as well publications available on SPE search platform. The research shows that main algorithms extensively studied found publications, which address anomaly problem, include Random Forest, Support Vector Machine, Long-term Memory Method Recurrent Neural Network. advantages disadvantages these methods briefly described. provides examples classical highly cited describe work along with articles illustrate their application industry. sections disciplines largest number using frequently used presented.

Language: Английский

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Machine Learning-based Anomaly Detection in IOT Sensing Devices for Optimal Security

Published: May 17, 2024

Language: Английский

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Clustering of Household Plots Using Self-organizing Maps

Smart innovation, systems and technologies, Journal Year: 2024, Volume and Issue: unknown, P. 463 - 476

Published: Jan. 1, 2024

Language: Английский

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Efficient Oil Tank Detection Using Deep Learning: A Novel Dataset and Deployment on Edge Devices

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 170346 - 170378

Published: Jan. 1, 2024

Language: Английский

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