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Ramasamy, Arun Kumar (2022) Intelligent real-time prediction for energy and sensing applications. MPhil thesis, University of Nottingham.

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Abstract

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.

Item Type:	Thesis (University of Nottingham only) (MPhil)
Supervisors:	Begam, Mumtaj
Keywords:	machine learning, oil and gas industry, pipeline inspection gauges
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculties/Schools:	University of Nottingham, Malaysia > Faculty of Science and Engineering — Engineering > Department of Electrical and Electronic Engineering
Item ID:	68937
Depositing User:	Ramasamy, Arun
Date Deposited:	24 Jul 2022 04:40
Last Modified:	24 Jul 2022 04:40
URI:	https://eprints.nottingham.ac.uk/id/eprint/68937

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