Successful application of AI techniques: A hybrid approach
It is common for a subject matter expert or a technician to analyse and evaluate all available data to make decisions about actions and interventions.
byTom Rhodes and Tony McGrail
Introduction
There is a large push to use artificial intelligence (AI) and machine learning (ML) to help reduce the time of performing maintenance on transformers and predicting where and when the next transformer will fail [1, 2, 3]. Major companies in different industries are promoting and telling the wonders of AI and ML: managing the replacement plans of an ageing or aged fleet, reduction in maintenance while extending asset life, operational efficiency, all while capturing the expertise available so that it is not lost. These are lofty goals, and claims are already being made for the benefits of AI applications in ‘the real world’. The problem we face is that AI is not perfect – it has its role in the analysis of well-described problems with sufficient data to cover all possible situations that may be found. Let us consider two things which are true in our industry:
- we are almost always faced with incomplete and possibly ambiguous data,
- the analysis of data does not take place in a vacuum as we have a history and a knowledgebase to call on to check the results.
So in simple terms, if an AI system is developed which analyses data for power transformers, then based on the data available it should be able to replicate what has already been developed as ‘common knowledge’ or industry expertise. For example, in DGA analysis, identifying increased levels of acetylene with increased probability of failure should be a rule which is identified [4]. If the AI is unable to state the rule in clear terms, then we may not trust other analyses described: we have to have a believable audit trail for the analysis to justify actions.
Business environment:
In an ideal world, we would have complete and detailed information on each of our transformers: maintenance history, test data, monitoring data, fault data, and so on. There would be standards and analytic tools to tell us about each individual transformer: the health, probability of failure, remaining life and so on. In practice, the data may be incomplete, inconsistent, or missing.
It is common for a subject matter expert (SME) or a technician to analyse and evaluate all available data to make decisions about actions and interventions in their region or area. Transformers would be ranked manually and grouped for prioritisation of maintenance, replacement or other intervention. Some of the analysis methods may be used only by some SMEs and not others, and they may have their own specific approaches meaning that analysis could be inconsistent based on the region and the individual involved. So, the push to more uniform approaches based on AI and ML seems both rational and sensible, especially as most experienced personnel, who understand the data, are retiring.
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