Page 15 - North American Clean Energy January/February 2019 Issue

the model becomes obsolete. Arti cial intelligence can solve these
challenges. AI enabled predictive maintenance platforms ingest sensor data, and automatically create models to identify when each individual asset is drifting away from its normal expected behavior. By its very nature, an AI model is continuously learning and re ning itself, allowing it
to analyze patterns and catch failures - especially if it has never seen them before. is exibility also allows a cognitive model to catch edge cases that a rules- based approach cannot. Even adjustments to the asset itself are not a problem, as the dynamic nature of these models allows them to adapt to changing conditions.
is new type of predictive maintenance
is already being deployed across the wind industry, to protect main bearings, pitch bearings, generators, gearboxes, and more.
AI-powered normal behavior modeling
AI-powered predictive maintenance
relies on normal behavior modeling
(NBM) to o er an innovative approach to anticipating secondary failures, as opposed to traditional condition monitoring. NBM uses historical sensor data and neural networks to more accurately characterize the normal operating behavior of a turbine subcomponent. An index is then developed based on the inherent risk of failure, depending on how far the observed asset behavior deviates from the derived norm.
Because behavior deviations can occur in both overt and covert ways, it is critical to understand the contributing features and indicators of failure, in order to better derive root cause and drive precise repair actions. rough
a concept called explainable AI, advanced machine learning platforms are able to surface and prioritize the top contributing features that are driving the results. Explainable AI provides clear and actionable insights to the operator, helping to prioritize their next steps.
In case studies leveraging this technology, AI-powered predictive maintenance has been able to detect main bearing failures months in advance, with up to 90 percent accuracy; it has ag impending generator failures with one month’s notice, at 70 percent accuracy.
Getting started with AI
How can operators obtain this technology for themselves?
To successfully implement an AI enabled predictive maintenance solution, a company needs to have a speci c problem for the AI to solve - and lots of good data (of a high enough quality) for an algorithm to learn
from. With wind farms, seasonality is important; having a full year of historical data improves the algorithm. An AI solution is only as good as the data it’s been given. ere also needs to be a
reasonable expectation of nding a pattern in the data; without that, there’s nothing for the AI to analyze.
Other important considerations include clear goals, strong leadership, and selecting a legitimate AI vendor with expertise in the area. Wind operators that pay attention to these factors, and plan their AI implementations accordingly, have the potential to completely reshape their operations, and improve their operating margins for the future.
Jessica Hamm is Director of Solutions for SparkCognition. SparkCognition works with companies around the world to build human-level intelligence applied at machine scale, in order to optimize operations, and nd new solutions to old problems.
SparkCognition /// www.sparkcognition.com
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North American Clean Energy
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