VROC's power generation client wished to investigate the ability for AI predictive analytics to assist with their asset health monitoring and predictive maintenance on critical assets, including their boiler pumps.
VROC set up AI models to monitor the pump health by ingesting all the data for the critical sensor tags corresponding to pump and motor bearing temperatures, as well as pump flowrate. The AI models would show any deviation from predicted values.
VROC's models detected a trend of deviation of actual temperatures increasing against predicted AI temperatures on both DE and NDE bearing temperatures. This trend existed for almost 2 months. In addition VROC's platform detected a deviation of the pump suction flowrate, which was higher than predicted, and meant that pump flowrate was reducing. A sudden DE temperature spike occurred which caused a trip.
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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