Published Work

Full text is available upon request

Engineering Governance Model and Computerised Monitoring System.

This paper proposes a standardised methodology for the investigation of catastrophic incidents based upon a set of traits derived from headings used by the Health and Safety Executive (HSE), and other specific engineering governance headings. This methodology is used to demonstrate the similarities between the Challenger disaster and the Nimrod XV230 disaster, and how measuring the impact levels of these traits would also aid the dissemination of investigation results and recommendations. Another use of standardised question sets would be to monitor the performance of an engineering business, as lead indicators, thus helping to identify the issues before they become much bigger problems.

Journal of Computing in Systems and Engineering
2009

Standardising criteria for disaster investigation to simplify the collection processing and dissemination of preventative information.

This paper proposes a standardised methodology for the investigation of catastrophic incidents based upon a set of traits derived from headings used by the Health and Safety Executive (HSE), and other specific engineering governance headings. This methodology is used to demonstrate the similarities between the Challenger disaster and the Nimrod XV230 disaster, and how measuring the impact levels of these traits would also aid the dissemination of investigation results and recommendations. Another use of standardised question sets would be to monitor the performance of an engineering business, as lead indicators, thus helping to identify the issues before they become much bigger problems.

Journal of Intelligent Mobility
2013

Engineering Governance through the Provision of Intelligent Monitoring Systems.

This paper describes the development of an intelligent monitoring system to monitor and predict potential situations that may, in the future, lead to a catastrophe. Based on documented lessons learned from past significant catastrophic incidents, the combinations of failures that have led to disasters can be grouped under three distinct engineering governance headings of “people,” “process” and “tools.” The intelligence uses data mining software to detect patterns in the collected information. The predictive capability is enhanced using trend analysis — and through the adaptation of this information using pre-processors — to find the type and amount of variance necessary to create a recognizable warning pattern. The type of information to collect and the particular data mining tools to use will be the subject of further research and testing.

International Journal of System Safety
2011

An Intelligent Monitoring System to Predict Potential Catastrophic Incidents.

This dissertation identified a gap in research for an intelligent monitoring system to monitor various indicators within complex engineering industries, in order to predict the potential situations that may lead to catastrophic failures. The accuracy of prediction was based upon lessons learnt from historic catastrophic incidents. These incidents are normally attributed to combinations of several minor errors or failures, and seldom occur through single point failures. The new system to monitor, identify and predict the conditions likely to cause a catastrophic failure could improve safety, reduce down time and prioritise funding. 

University of Portsmouth PhD Thesis
2014

Disaster Prevention Through Intelligent Monitoring

Despite various tools and systems that can monitor complex engineering environments, bad things still happen regularly in all types of engineering industries. An intelligent system designed to monitor certain indicators, regardless of engineering industry, that might predict catastrophes would ultimately reduce the potential for loss of human life and property. In this article, 10 catastrophes were researched to identify their root causes and the various root cause combinations. These documented catastrophes covered a broad spectrum of engineering including oil, gas, nuclear, rail, air and space. The root causes identified in the investigation reports were grouped under 10 trait headings and their efficacy was tested using a qualitative fault tree of a credible catastrophic failure scenario. Each trait was adjusted to signify various levels of failure and fed into the prototype system representing the fault tree. While near real-time monitoring and trend analysis was investigated and shown to support an intelligent system that might predict catastrophe, one of the surprising additional results from the research was highlighting the need to standardize the approach to investigative reports and audits of existing systems. Reporting in the same “technical language” and looking for specific condition levels for each of the traits could provide a true picture of asset condition and the required funding prioritization, as well as assisting the dissemination of findings to all engineering industries.

International Journal of System Safety
2017
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