Identifying potential problems and a predicted time to failure enables advance ordering of replacement parts and scheduled proactive maintenance, reducing unexpected downtime.
An AI application that can be set up easily and provide proper diagnosis of issues, with continuous self-learning, can eliminate time-consuming maintenance and also diagnose reliability issues.
In a new article in Process Engineering magazine, Alan Cambridge of Peacock Engineering, Jason Tweedy of Brammer Buck & Hickman, and Andrew Normand of Encora Energy discuss the benefits of AI in the management and maintenance of physical assets.
Alan addresses how there can be a ‘self learning gap’ in industries where assets do not (and cannot) fail on a regular basis (such as power generation), and how mobile and SCADA data can help to bridge that gap.
Read the article here (opens in a new tab)
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