Prescriptive maintenance (RxM) couples highly sophisticated analytics with Big Data to prescribe corrective actions. New and emerging prescriptive analytics solutions are making RxM a viable goal. Their automated intelligence and ability to reconcile exponentially growing data from the internet of things (IoT), far-flung existing data silos, and untapped workforce knowledge has these tools drawing increasing interest.
Bentley’s AssetWise CONNECT Edition software, powered by the Microsoft Azure Machine Learning platform, uses situational intelligence to provide early warning of asset and operational issues, augment and expedite decision-making, and guide actions.
Emerson’s emerging asset platforms, such as Plantweb Optics, are data aggregators that eliminate data silos within plants and across the enterprise. Mani Janardhanan, Plantweb IIoT product director at Emerson, explains that most plants today have data silos, many of which are functional. They successfully provide alerts to asset faults that could lead to outright failures. “Prescriptive maintenance asks us to take the next step,” he says.
“Now we are working with customers to move beyond predicting failure to prescribing a resolution that is business- and safety-efficient,” says Janardhanan. This is achieved by connecting “asset data and history from many plants with analytics to create insight-driven, prescribed responses.”
Greg Perry, senior consultant for Fluke Accelix, believes RxM represents the dawn of autonomous reliability. “With machine learning and improved data analysis, powerful insights into autonomous corrective, preventive, or predictive actions can be precisely aligned,” he says. “As technology improves and organizations adopt these advanced condition-based methods, downtime and breakdowns will result in smaller margins of random failures.”