Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry
Building and repair of ships are considered the Evergreen industry nationally as well as globally. The ships are generally gone in by the periodic scheduled repairs by the Indian shipbuilding industries. Sometimes industries lack productivity and lack of modernization some modern methods should b...
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University of Baghdad, College of Science for Women
2024-08-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9260 |
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| author | PNV Srinivasa Rao PVY Jayasree |
| author_facet | PNV Srinivasa Rao PVY Jayasree |
| author_sort | PNV Srinivasa Rao |
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Building and repair of ships are considered the Evergreen industry nationally as well as globally. The ships are generally gone in by the periodic scheduled repairs by the Indian shipbuilding industries. Sometimes industries lack productivity and lack of modernization some modern methods should be followed. The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy. The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. By evaluating the performance results and confusion matrix through the training and testing output all the metrics for training and testing are classified in the confusion matrix. Our proposed predictive maintenance model with high accuracy for the detection of failures in earlier stages and maintenance of Indian ships can help in the avoidance of accidents in voyages and the loss of goods and money during transportation. The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models. The proposed Pd-MaaS helps in early detection of failures in the ships which is the greatest advantage in the Indian shipbuilding industry.
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| format | Article |
| id | doaj-art-29fcc8faa9e1441ca56922f82e20d797 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-29fcc8faa9e1441ca56922f82e20d7972025-08-20T03:58:10ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862024-08-0121810.21123/bsj.2024.9260Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building IndustryPNV Srinivasa Rao0https://orcid.org/0000-0002-3464-2517PVY Jayasree1https://orcid.org/0000-0002-9576-637XDepartment of EECE, GITAM Institute of Technology, GITAM University, Visakhapatnam, India.Department of EECE, GITAM Institute of Technology, GITAM University, Visakhapatnam, India. Building and repair of ships are considered the Evergreen industry nationally as well as globally. The ships are generally gone in by the periodic scheduled repairs by the Indian shipbuilding industries. Sometimes industries lack productivity and lack of modernization some modern methods should be followed. The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy. The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. By evaluating the performance results and confusion matrix through the training and testing output all the metrics for training and testing are classified in the confusion matrix. Our proposed predictive maintenance model with high accuracy for the detection of failures in earlier stages and maintenance of Indian ships can help in the avoidance of accidents in voyages and the loss of goods and money during transportation. The validation of the proposed predictive maintenance model optimization with different types of deep learning algorithms shows that our proposed methodology gives an improved accuracy of 98.9336% which is higher than any other models. The proposed Pd-MaaS helps in early detection of failures in the ships which is the greatest advantage in the Indian shipbuilding industry. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9260Indian Shipbuilding, Jellyfish Optimization, Machine Learning, Predictive Maintenance as a Service, RNN-LSTM |
| spellingShingle | PNV Srinivasa Rao PVY Jayasree Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry مجلة بغداد للعلوم Indian Shipbuilding, Jellyfish Optimization, Machine Learning, Predictive Maintenance as a Service, RNN-LSTM |
| title | Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry |
| title_full | Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry |
| title_fullStr | Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry |
| title_full_unstemmed | Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry |
| title_short | Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry |
| title_sort | development of iiot based pd maas using rnn lstm model with jelly fish optimization in the indian ship building industry |
| topic | Indian Shipbuilding, Jellyfish Optimization, Machine Learning, Predictive Maintenance as a Service, RNN-LSTM |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9260 |
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