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Increasing the adoption of prognostic systems for heath management in the power industry

Catterson, Victoria and Costello, Jason and West, Graeme and McArthur, Stephen and Wallace, Christopher (2013) Increasing the adoption of prognostic systems for heath management in the power industry. In: PHM 2013 Prognostics and System Health Management Conference, 2013-09-08 - 2013-09-11.

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Abstract

Effective asset management benefits from accurate information about the current health and expected future health of each asset. This information can be supplied by diagnostic and prognostic systems respectively, utilizing data from condition monitoring systems and inspections to derive status and likely future behavior. However, new prognostic techniques can face difficulties when transitioning from research tool to industrial deployment. Within the power industry there are two main drivers to improve asset management. First, the safety of personnel and members of the public may be compromised by a failure in service, so maintenance aims to pre-empt such failures. Secondly, more accurate prediction of future degradation can feed into a more efficient maintenance program, reducing costs through appropriate delay of repair and replacement. These drivers prompt a cautious interest in prognostic tools, countered by some reticence within traditional utilities to adopt new and relatively unproven technologies. This paper identifies issues which may hinder the deployment of prognostic techniques within the power industry. It considers two examples of diagnostic systems (for rotating plant within nuclear stations, and for power transformers) which progressed past the research prototype stage to deployed demonstrator systems, and one example of a prognostic system (for HV circuit breakers) which has not yet made that transition. Drawing lessons from these experiences, the paper extracts key factors which link the deployed systems, but are missing from the third. The paper concludes that structural issues are the main differentiators, including automated access to data, clear and concise user interfaces, and working with the engineers who will ultimately use the system. Trust in the algorithm is also important, and the paper outlines the techniques selected for the case study applications, with discussion of selected results. Once engineers are able to integrate prognostics into their processes, it naturally contributes to asset management strategy.