Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation
Vessels frequently encounter challenging marine conditions that expose the propeller-hull to corrosive water and marine fouling. These challenges necessitate innovative approaches to optimize propeller-hull performance. This study aims to assess a method for predicting propeller-hull degradation. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8956 |
| _version_ | 1797631238097338368 |
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| author | Christos Spandonidis Dimitrios Paraskevopoulos |
| author_facet | Christos Spandonidis Dimitrios Paraskevopoulos |
| author_sort | Christos Spandonidis |
| collection | DOAJ |
| description | Vessels frequently encounter challenging marine conditions that expose the propeller-hull to corrosive water and marine fouling. These challenges necessitate innovative approaches to optimize propeller-hull performance. This study aims to assess a method for predicting propeller-hull degradation. The proposed solution revolves around an innovative Key Performance Indicator (KPI) based on Artificial Neural Networks (ANNs). Our objective is to validate the findings; thus, a thorough comparison is conducted between the proposed method and the baseline solution derived from the ISO-19030. Emphasis is placed on determining the optimal parameters for computing the KPI, which involves applying various features, filters, and pre-processing techniques. The proposed method is tested on real data collected by an Internet of Things (IoT) system installed in different types of vessels. Four distinct experiments with ANNs are conducted. Results demonstrate that the ANN-based indicator offers greater accuracy in predicting propeller-hull degradation compared to the baseline method. Additionally, it is demonstrated that selecting a diverse set of features and implementing consistent filtering and preprocessing techniques enhance the performance of the traditional indicator. The utilization of Deep Learning (DL) in the maritime industry is of great significance, as it enables a comprehensive and dynamic assessment of predictive maintenance of the propeller-hull. The DL index method holds potential for diverse maintenance applications, providing a holistic platform with anticipated environmental and financial benefits. |
| first_indexed | 2024-03-11T11:20:59Z |
| format | Article |
| id | doaj.art-096bdfdc1a884db696b44b1d98d2e63f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:20:59Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-096bdfdc1a884db696b44b1d98d2e63f2023-11-10T15:12:55ZengMDPI AGSensors1424-82202023-11-012321895610.3390/s23218956Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull DegradationChristos Spandonidis0Dimitrios Paraskevopoulos1Prisma Electronics S.A., Research, and Development, 87 Democratias Avenue, 68132 Alexandroupolis, GreecePrisma Electronics S.A., Research, and Development, 87 Democratias Avenue, 68132 Alexandroupolis, GreeceVessels frequently encounter challenging marine conditions that expose the propeller-hull to corrosive water and marine fouling. These challenges necessitate innovative approaches to optimize propeller-hull performance. This study aims to assess a method for predicting propeller-hull degradation. The proposed solution revolves around an innovative Key Performance Indicator (KPI) based on Artificial Neural Networks (ANNs). Our objective is to validate the findings; thus, a thorough comparison is conducted between the proposed method and the baseline solution derived from the ISO-19030. Emphasis is placed on determining the optimal parameters for computing the KPI, which involves applying various features, filters, and pre-processing techniques. The proposed method is tested on real data collected by an Internet of Things (IoT) system installed in different types of vessels. Four distinct experiments with ANNs are conducted. Results demonstrate that the ANN-based indicator offers greater accuracy in predicting propeller-hull degradation compared to the baseline method. Additionally, it is demonstrated that selecting a diverse set of features and implementing consistent filtering and preprocessing techniques enhance the performance of the traditional indicator. The utilization of Deep Learning (DL) in the maritime industry is of great significance, as it enables a comprehensive and dynamic assessment of predictive maintenance of the propeller-hull. The DL index method holds potential for diverse maintenance applications, providing a holistic platform with anticipated environmental and financial benefits.https://www.mdpi.com/1424-8220/23/21/8956maintenanceprognosisKPIsArtificial Neural NetworksISO-19030propeller-hull |
| spellingShingle | Christos Spandonidis Dimitrios Paraskevopoulos Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation Sensors maintenance prognosis KPIs Artificial Neural Networks ISO-19030 propeller-hull |
| title | Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation |
| title_full | Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation |
| title_fullStr | Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation |
| title_full_unstemmed | Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation |
| title_short | Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation |
| title_sort | evaluation of a deep learning based index for prognosis of a vessel s propeller hull degradation |
| topic | maintenance prognosis KPIs Artificial Neural Networks ISO-19030 propeller-hull |
| url | https://www.mdpi.com/1424-8220/23/21/8956 |
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