Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGN_167 | |
| Title | Re-Defining Failure | |
| Status | Completed | |
| Energy Categories | Fossil Fuels: Oil Gas and Coal(Oil and Gas, Refining, transport and storage of oil and gas) 100%; | |
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | SOCIAL SCIENCES (Business and Management Studies) 25%; PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 25%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 25%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given Northern Gas Networks |
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| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 August 2016 | |
| End Date | 01 January 2017 | |
| Duration | 7 months | |
| Total Grant Value | £185,524 | |
| Industrial Sectors | Technical Consultancy | |
| Region | Yorkshire & Humberside | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , Northern Gas Networks (100.000%) |
| Web Site | http://www.smarternetworks.org/project/NIA_NGN_167 |
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| Objectives | The objectives of this phase of work across work packages 1 and 2 are to: Establish a cross-utility understanding of asset failureProduce a clear and actionable definition of failure that accounts for asset maintenanceProduce an independent peer review of good practice in deterioration modelling techniques and relevant statistical analysisrovide a fundamental resource and key dataset which is software agnostic and builds NGN capabilityUse an innovative dynamic representation of equipment/component level replacement/maintenance to enable what-if scenario analysis; Demonstrate the connection of models to understand within-site redundancy and failure cascades; Establish quantitatively true failure rates for assets not run to failure considering maintenance programs; Demonstrate the ability for rapid scenario analysis to inform investment, maintenance and resource planning to result in an improved maintenance resource utilisation; Establish a key stepping stone that would allow future integration of a systems approach to optimising asset investment and maintenance The project will be deemed successful, ifKnowledge on advanced statistical modelling approaches to the NGN project team has been transferredStatistical modelling approaches that can be integrated with existing and future systems/tools have been deliveredThe potential benefit of application of advanced statistical modelling approaches developed through the project has been quantified | |
| Abstract | Through the development of the Network Output Measure Risk Trading Methodology (NOMs) there has been no clearly agreed methodology to calculate the probability of failure (PoF), deterioration rates, or impact of failure (IoF) for asset classes that GDNs typically do not run to failure because of the maintenance regimes in place, e. g. LTS pipelines, or PRS regulators and slam shuts. This means, for example, that the underlying deterioration of the asset is mitigated by maintenance activity. The current approach to assessing the PoF for maintainable assets (MAs) is to use expert knowledge to determine information about key factors, such as condition grades, time between failures, time to repair etc. These factors are then used to elicit PoF and deterioration curves. This approach does not fully take account of the impact of maintenance regimes on the PoF of an asset or the interdependencies of risk. As such, there is the potential to over or under estimate the risk of asset failure which can lead to non-essential expenditure, inefficiency and poorly informed decision making. In addition, the current approach does not quantify the redundancy and interdependency with asset systems (e. g. complete PRS sites) or across networked assets. This may result in risk being overestimated where system and network redundancy is mitigating risk, or underestimation of risk where failure cascades can be caused by a single point of failureThrough the development of the Network Output Measure Risk Trading Methodology (NOMs) there has been no clearly agreed methodology to calculate the probability of failure (PoF), deterioration rates, or impact of failure (IoF) for asset classes that GDNs typically do not run to failure because of the maintenance regimes in place, e. g. LTS pipelines, or PRS regulators and slam shuts. This means, for example, that the underlying deterioration of the asset is mitigated by maintenance activity. The current approach to assessing the PoF for maintainable assets (MAs) is to use expert knowledge to determine information about key factors, such as condition grades, time between failures, time to repair etc. These factors are then used to elicit PoF and deterioration curves. This approach does not fully take account of the impact of maintenance regimes on the PoF of an asset or the interdependencies of risk. As such, there is the potential to over or under estimate the risk of asset failure which can lead to non-essential expenditure, inefficiency and poorly informed decision making. In addition, the current approach does not quantify the redundancy and interdependency with asset systems (e. g. complete PRS sites) or across networked assets. This may result in risk being overestimated where system and network redundancy is mitigating risk, or underestimation of risk where failure cascades can be caused by a single point of failure This project will start by re-defining failure. There is a lack of data associated with the traditional failure of the types of asset mentioned above as they are maintained back to condition. However, if failure is re-defined to be the failure of normal functioning, then data, such as fault and current maintenance regime data, can be analysed to help inform more efficient Totex management of these assets. This project aims to remove subjective analysis and take the analysis of failure back to a data driven approach to determine the true deterioration, incorporating the effect of external factors, including maintenance regimes. It is intended that the analysis will be performed at an individual asset level rather than a grouped or cohort level. To understand the failure of systems, this project will use individual asset failure models to model the interdependencies and redundancies at an asset system level, e. g. for PRS this will be at functional location/site level.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 05/12/18 | |
