Intelligent real-time prediction for energy and sensing applications
The advancements of science and technology has been rapid since the boom of the fourth industrial revolution that began arguable about a decade ago. As smart hardware and software began to be paired together over high speed transfer of information, the world of innovation witnessed the rise of Big D...
| Main Author: | |
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2022
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| Online Access: | https://eprints.nottingham.ac.uk/68937/ |
| _version_ | 1848800516519231488 |
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| author | Ramasamy, Arun Kumar |
| author_facet | Ramasamy, Arun Kumar |
| author_sort | Ramasamy, Arun Kumar |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The advancements of science and technology has been rapid since the boom of the fourth industrial revolution that began arguable about a decade ago. As smart hardware and software began to be paired together over high speed transfer of information, the world of innovation witnessed the rise of Big Data and Machine Learning.
These 2 heroes of the 21st centuries have been widely embraced and adopted in various industries, resulting in innovations and outcomes that we never could have perceived otherwise, especially in closing the gaps between probability and predictability i.e. stock market predictions and such.
However, one gigantic industry that has yet to reap on the offerings of Big Data and Machine Learning is the oil and gas industry. As extreme a form of engineering it is, methods and technologies are still primarily mechanically driven, specifically when it comes to safety and preventive measures i.e. in failure prediction efforts. Manual methods using predated technologies are still industry standard for many applications within industry.
This research takes a look at these current methods, and proposes a new way of performing failure prediction analysis using machine learning. |
| first_indexed | 2025-11-14T20:52:48Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-68937 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:52:48Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-689372022-07-24T04:40:05Z https://eprints.nottingham.ac.uk/68937/ Intelligent real-time prediction for energy and sensing applications Ramasamy, Arun Kumar The advancements of science and technology has been rapid since the boom of the fourth industrial revolution that began arguable about a decade ago. As smart hardware and software began to be paired together over high speed transfer of information, the world of innovation witnessed the rise of Big Data and Machine Learning. These 2 heroes of the 21st centuries have been widely embraced and adopted in various industries, resulting in innovations and outcomes that we never could have perceived otherwise, especially in closing the gaps between probability and predictability i.e. stock market predictions and such. However, one gigantic industry that has yet to reap on the offerings of Big Data and Machine Learning is the oil and gas industry. As extreme a form of engineering it is, methods and technologies are still primarily mechanically driven, specifically when it comes to safety and preventive measures i.e. in failure prediction efforts. Manual methods using predated technologies are still industry standard for many applications within industry. This research takes a look at these current methods, and proposes a new way of performing failure prediction analysis using machine learning. 2022-07-24 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/68937/1/MPhil_Thesis_Arun_2022.pdf Ramasamy, Arun Kumar (2022) Intelligent real-time prediction for energy and sensing applications. MPhil thesis, University of Nottingham. machine learning oil and gas industry pipeline inspection gauges |
| spellingShingle | machine learning oil and gas industry pipeline inspection gauges Ramasamy, Arun Kumar Intelligent real-time prediction for energy and sensing applications |
| title | Intelligent real-time prediction for energy and sensing applications |
| title_full | Intelligent real-time prediction for energy and sensing applications |
| title_fullStr | Intelligent real-time prediction for energy and sensing applications |
| title_full_unstemmed | Intelligent real-time prediction for energy and sensing applications |
| title_short | Intelligent real-time prediction for energy and sensing applications |
| title_sort | intelligent real-time prediction for energy and sensing applications |
| topic | machine learning oil and gas industry pipeline inspection gauges |
| url | https://eprints.nottingham.ac.uk/68937/ |