1D CNN Model for Corrosion Prediction
The large number of pipelines that have in this world have led to create a prediction system which can help in keep maintaining the condition of pipelines. Then, the used of advanced analytics can help to uncover patterns that not revealed by conventional analytics and predictive analytics are...
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| Format: | Final Year Project |
| Language: | English |
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IRC
2020
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| Online Access: | http://utpedia.utp.edu.my/21797/1/23283_Nurfaraheen%20Zaini.pdf http://utpedia.utp.edu.my/21797/ |
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my-utp-utpedia.217972021-09-24T09:56:52Z http://utpedia.utp.edu.my/21797/ 1D CNN Model for Corrosion Prediction Zaini, Nurfaraheen Q Science (General) The large number of pipelines that have in this world have led to create a prediction system which can help in keep maintaining the condition of pipelines. Then, the used of advanced analytics can help to uncover patterns that not revealed by conventional analytics and predictive analytics are the future pipeline condition assessment and monitoring that will provide stakeholders with a better overview of operations, more control and flexibility for managing pipeline assets. Besides, it is to develop a machine learning of predictive analytics algorithm with a visualization dashboard for the predicted of internal and external corrosion levels of pipelines. It can help to enhance the integrity management of strategic pipelines as well. IRC 2020-01 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21797/1/23283_Nurfaraheen%20Zaini.pdf Zaini, Nurfaraheen (2020) 1D CNN Model for Corrosion Prediction. IRC, Universiti Teknologi PETRONAS. (Submitted) |
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| language |
English |
| topic |
Q Science (General) |
| spellingShingle |
Q Science (General) Zaini, Nurfaraheen 1D CNN Model for Corrosion Prediction |
| description |
The large number of pipelines that have in this world have led to create a
prediction system which can help in keep maintaining the condition of
pipelines. Then, the used of advanced analytics can help to uncover patterns
that not revealed by conventional analytics and predictive analytics are the
future pipeline condition assessment and monitoring that will provide
stakeholders with a better overview of operations, more control and flexibility
for managing pipeline assets. Besides, it is to develop a machine learning of
predictive analytics algorithm with a visualization dashboard for the predicted
of internal and external corrosion levels of pipelines. It can help to enhance the
integrity management of strategic pipelines as well. |
| format |
Final Year Project |
| author |
Zaini, Nurfaraheen |
| author_facet |
Zaini, Nurfaraheen |
| author_sort |
Zaini, Nurfaraheen |
| title |
1D CNN Model for Corrosion Prediction |
| title_short |
1D CNN Model for Corrosion Prediction |
| title_full |
1D CNN Model for Corrosion Prediction |
| title_fullStr |
1D CNN Model for Corrosion Prediction |
| title_full_unstemmed |
1D CNN Model for Corrosion Prediction |
| title_sort |
1d cnn model for corrosion prediction |
| publisher |
IRC |
| publishDate |
2020 |
| url |
http://utpedia.utp.edu.my/21797/1/23283_Nurfaraheen%20Zaini.pdf http://utpedia.utp.edu.my/21797/ |
| _version_ |
1739832913890902016 |
| score |
13.211869 |