A Framework for Designing Prognostic Systems

Prognostics is an emerging field within equipment condition monitoring, which looks to predict the occurrence of a failure ahead of time. When a fault develops there is generally some period of time before failure, where the equipment can continue to function even although its condition is deteriorating. For many years, diagnostic systems have tried to identify the specific fault that is occurring during this period of deterioration.

Prognostic systems aim to move beyond diagnosis, and predict the remaining life of the equipment. With a better understanding of the time until failure, the asset owner can schedule maintenance or replacement more effectively, to extract the full life from the equipment while reducing the chance of a failure in service. This in turn reduces costs associated with periodic maintenance and early replacement of assets.

Within the power industry, the need for accurate prognostics is pressing. National Grid statistics show that the majority of power transformers were installed before 1970, and thus exceed their original design life. Continued service relies on confident predictions about future health, traditionally provided by engineering judgement. At the opposite extreme, new technologies such as HVDC and offshore wind introduce new assets and new uses of established assets, where little operational experience can provide such engineering judgement. Both situations would be enhanced by the widespread adoption of prognostic systems.

Currently, there exist no standards or common approaches to developing prognostic systems. Such systems have been developed for a number of applications, but the lack of commonly-agreed terminology makes it difficult to compare approaches and methods. When faced with a new application, the designer must establish their own needs and requirements without any support or guidance. It is difficult to ensure all possible options have been adequately considered.

This research aims to create a design framework for prognostic systems in the power industry. Such a framework can be used as a methodology for developing new prognostic systems, guiding the designer through different options and design decisions. The framework will also include the terminology needed to describe and compare different prognostic approaches and system components, allowing assessment of advantages and disadvantages of different choices. The existence of a design framework will make it faster and easier to build prognostic systems, leading to wider deployment of this technology, and ultimately better maintenance scheduling.

The short term beneficiaries of this research will include companies investigating the potential of prognostic monitoring, such as GSE Systems and National Grid. These two companies will gain direct benefit, as they have each funded other research projects to develop specific prognostic systems, and these systems will be two of the case studies used to develop the design framework. As a result, these companies gain extra analysis of their prognostic systems, in return for their in-kind support (access to engineering expertise, etc) as detailed in Pathways to Impact.

In the medium term, this research will show that prognostic systems are viable for industrial applications, and that the process of system design can be structured and robust. This will build confidence in the use of prognostics, and allow asset-intensive companies to begin to transition away from diagnostics-based maintenance scheduling to prognostic systems. Ultimately, this translates to a more reliable and secure supply of electricity.

Within the period of the grant, societal benefits will come through involvement of the researcher in the Engineering Education Scheme: an engineering outreach programme which matches mentors with groups of school pupils to solve an engineering challenge. As detailed in the Pathways to Impact, the researcher will gain experience of talking about their work in a non-academic setting, and the school pupils will gain a hands-on understanding of what engineering involves.