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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/33922

Title: Evolution of maintenance strategies in oil and gas industries: the present achievements and future trends
Authors: Eyoh, Jeremiah
Kalawsky, Roy S.
Keywords: Availability
Intelligent maintenance
Maintenance techniques
Neural network
Oil and gas
Sensor fusion
Issue Date: 2018
Citation: EYOH, J. and KALAWSKY, R.S., 2018. Evolution of maintenance strategies in oil and gas industries: the present achievements and future trends. Presented at the FEAST International Conference on Engineering Management, Industrial Technology, Applied Sciences, Communications and Media (EITAC), London, UK, 28-29 July 2018.
Abstract: Engineering Systems maintenance and reliability challenges have drawn serious attention of researchers and industrialists all over the world due to continuous evolution, innovation and complexity of modern technologies deployed in manufacturing and production systems. These systems need very high reliability and availability due to business, mission and safety critical nature of their operations. This paper reviews evolution of systems or equipment maintenance strategies practiced over the years in complex industrial and manufacturing systems such as oil and gas production systems, satellite communication system, spacecraft navigational system, nuclear power plants, etc. The paper also examines the current maintenance and reliability philosophies, their limitations and highlights major breakthroughs and achievements with regards to complex engineering systems maintenance. Intelligent maintenance, a novel approach to complex engineering systems maintenance and reliability sustainment is proposed. The proposed approach reintegrates operation and maintenance phase into system development life cycle, adopts advanced engineering tools and methodology in developing condition-based predictive maintenance, an intelligent maintenance system with resilient, autonomous and adaptive capabilities. Application of Neural network approach to multisensor data fusion for condition-based predictive maintenance system is briefly presented.
Description: This is a conference paper.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/33922
Publisher Link: http://forum-east.com/eitac-july-2018-event/
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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