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Reliability and process engineers need solutions that fit their direct needs, presenting them with ease-of-use and domain familiarity. Such solutions are the only way to provide the scalability to efficiently solve plant problems. Engineers, who are doing the data science and engineering, can readily use built-in guardrails to solve role and domain specific problems that assure they find causation, and not just simple correlation. Such is the promise of role-based applications.
Accordingly, we see specific applications creating and deploying hundreds or thousands of Autonomous Agents–small software programs performing repetitive analysis, so the end user does not have to. Different Agents help users in specific ways. For mechanical problems, Agents scan signals to predict and prevent degradation and failure. Quality Agents warn of process conditions that reduce quality or yield and advise the changes to keep the manufacturing on track. Event Agents warn process operators when sensor patterns show errant conditions and advise the steps to prevent them from becoming major issues. More Agents will follow.
Sometimes more complex repetitive patterns occur across multiple similar assets such as repeat failures of the same type in power generating wind farms. Such patterns are then referred to data science teams to search for external influential factors, allowing business leaders to use the output analysis for determining the best path forward.
