A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations
Marine engineering officers operate and maintain the ship’s machinery during normal navigation. Most accidents on board are related to human factors which, at the same time, are associated with the workload of the crew members and the working environment. The number of alarms is so high that, most o...
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/6957 |
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| author | José A. Orosa Genaro Cao-Feijóo Francisco J. Pérez-Castelo José M. Pérez-Canosa |
| author_facet | José A. Orosa Genaro Cao-Feijóo Francisco J. Pérez-Castelo José M. Pérez-Canosa |
| author_sort | José A. Orosa |
| collection | DOAJ |
| description | Marine engineering officers operate and maintain the ship’s machinery during normal navigation. Most accidents on board are related to human factors which, at the same time, are associated with the workload of the crew members and the working environment. The number of alarms is so high that, most of the time, instead of helping to prevent accidents, it causes more stress for crew members, which can result in accidents. Convolutional Neural Networks (CNNs) are being employed in the recognition of images, which depends on the quality of the images, the image recognition algorithm, and the very complex configuration of the neural network. This research study aims to develop a user-friendly image recognition tool that may act as a visual sensor of alarms adjusted to the particular needs of the ship operator. To achieve this, a marine engineering simulator was employed to develop an image recognition tool that advises marine engineering officers when they are conducting their maintenance activities, with the aim to reduce their stress as a work risk prevention tool. Results showed adequate accuracy for three-layer Convolutional Neural Networks and balanced data, and the use of external cameras stands out for user-friendly applications. |
| format | Article |
| id | doaj-art-623b8ac411964063bae365e6a971ca52 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-623b8ac411964063bae365e6a971ca522024-11-08T14:41:39ZengMDPI AGSensors1424-82202024-10-012421695710.3390/s24216957A New Deep Learning Methodology for Alarm Supervision in Marine Power StationsJosé A. Orosa0Genaro Cao-Feijóo1Francisco J. Pérez-Castelo2José M. Pérez-Canosa3Department of Navigation Sciences and Marine Engineering, University of A Coruña, Paseo de Ronda, 51, 15011 A Coruña, SpainDepartment of Navigation Sciences and Marine Engineering, University of A Coruña, Paseo de Ronda, 51, 15011 A Coruña, SpainDepartment of Industrial Engineering, University of A Coruña, 15405 A Coruña, SpainDepartment of Navigation Sciences and Marine Engineering, University of A Coruña, Paseo de Ronda, 51, 15011 A Coruña, SpainMarine engineering officers operate and maintain the ship’s machinery during normal navigation. Most accidents on board are related to human factors which, at the same time, are associated with the workload of the crew members and the working environment. The number of alarms is so high that, most of the time, instead of helping to prevent accidents, it causes more stress for crew members, which can result in accidents. Convolutional Neural Networks (CNNs) are being employed in the recognition of images, which depends on the quality of the images, the image recognition algorithm, and the very complex configuration of the neural network. This research study aims to develop a user-friendly image recognition tool that may act as a visual sensor of alarms adjusted to the particular needs of the ship operator. To achieve this, a marine engineering simulator was employed to develop an image recognition tool that advises marine engineering officers when they are conducting their maintenance activities, with the aim to reduce their stress as a work risk prevention tool. Results showed adequate accuracy for three-layer Convolutional Neural Networks and balanced data, and the use of external cameras stands out for user-friendly applications.https://www.mdpi.com/1424-8220/24/21/6957control systemshipsCNNpower stationrisk prevention |
| spellingShingle | José A. Orosa Genaro Cao-Feijóo Francisco J. Pérez-Castelo José M. Pérez-Canosa A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations Sensors control system ships CNN power station risk prevention |
| title | A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations |
| title_full | A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations |
| title_fullStr | A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations |
| title_full_unstemmed | A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations |
| title_short | A New Deep Learning Methodology for Alarm Supervision in Marine Power Stations |
| title_sort | new deep learning methodology for alarm supervision in marine power stations |
| topic | control system ships CNN power station risk prevention |
| url | https://www.mdpi.com/1424-8220/24/21/6957 |
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