Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry
The electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy work...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/630 |
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| author | Dongyeop Lee Daesik Lim Joonwon Lee |
| author_facet | Dongyeop Lee Daesik Lim Joonwon Lee |
| author_sort | Dongyeop Lee |
| collection | DOAJ |
| description | The electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy working environment, but it is difficult to identify the potential health and safety risks present in their workplace and take appropriate action to keep their workers free from harm. Therefore, this paper proposes a novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry. It can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data created by the risk level of a current task, a worker profile, and the output of an on-site artificial intelligence (AI) engine in the SMGs. We practically implemented the proposed SAP architecture using the Hadoop ecosystem and verified its feasibility through a performance evaluation of the on-site AI engine and real-time operation of risk assessment and alarm notification for data-driven risk management. |
| format | Article |
| id | doaj-art-1e3c575076504539ab454e63308e0b03 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1e3c575076504539ab454e63308e0b032025-01-24T13:20:11ZengMDPI AGApplied Sciences2076-34172025-01-0115263010.3390/app15020630Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power IndustryDongyeop Lee0Daesik Lim1Joonwon Lee2Convergence Technology Laboratory, KEPCO Research Institute, Daejeon 34056, Republic of KoreaConvergence Technology Laboratory, KEPCO Research Institute, Daejeon 34056, Republic of KoreaDepartment of Safety Health Convergence Engineering, Soongsil University, Seoul 06978, Republic of KoreaThe electric power industry poses significant risks to workers with a wide range of hazards such as electrocution, electric shock, burns, and falls. Regardless of the types and characteristics of these hazards, electric power companies should protect their workers and provide a safe and healthy working environment, but it is difficult to identify the potential health and safety risks present in their workplace and take appropriate action to keep their workers free from harm. Therefore, this paper proposes a novel safety autonomous platform (SAP) for data-driven risk management in the electric power industry. It can automatically and precisely provide a safe and healthy working environment with the cooperation of safety mobility gateways (SMGs) according to the safety rule and risk index data created by the risk level of a current task, a worker profile, and the output of an on-site artificial intelligence (AI) engine in the SMGs. We practically implemented the proposed SAP architecture using the Hadoop ecosystem and verified its feasibility through a performance evaluation of the on-site AI engine and real-time operation of risk assessment and alarm notification for data-driven risk management.https://www.mdpi.com/2076-3417/15/2/630electric power industryensemble AIrisk managementsafety platformsafety rule |
| spellingShingle | Dongyeop Lee Daesik Lim Joonwon Lee Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry Applied Sciences electric power industry ensemble AI risk management safety platform safety rule |
| title | Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry |
| title_full | Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry |
| title_fullStr | Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry |
| title_full_unstemmed | Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry |
| title_short | Safety Autonomous Platform for Data-Driven Risk Management Based on an On-Site AI Engine in the Electric Power Industry |
| title_sort | safety autonomous platform for data driven risk management based on an on site ai engine in the electric power industry |
| topic | electric power industry ensemble AI risk management safety platform safety rule |
| url | https://www.mdpi.com/2076-3417/15/2/630 |
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