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Click this link to view the Whitepaper: The Business Case for Machine Learning in Predictive Maintenance, a copy will also be sent to your inbox shortly.


Why not jump over to our  customer case studies where you can see how VROC’s predictive maintenance solution has been used to improve asset reliability, failure avoidance and to identify the root cause of a complex operational problem.  

Customer Reviews

Our client's success is our success

“We have prolonged the gas compressor reliability to four months, from a maximum of 2 weeks running. The GCM uptime has improved to a value of 21.7m USD.”

Head of Offshore Operations

“Let everyone use, don’t restrict to any process engineer or operation engineer, give everybody access including business planners, let everyone use it. Because the beauty of this is that it will open the eyes of the importance of Artificial Intelligence in Oil and Gas.”

Manager Head Strategy & Performance Health Safety & Environment, Gas and New Energy

“Most companies have struggled to address all the potential opportunities for data science in their business, because there are so many, and then they struggle to operationalize those models and make them into applications which users, engineers, maintainers and reliability engineers can make use of… The advent of autoML is trying to lift and remove some of the challenges of scaling and operationalizing models.”

Denis Marshment, previous Global Vice President – Data Science Customer Solutions, Worley
Useful resources

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Can we kill shutdowns? Can predictive maintenance stop downtime?

Downtime is bad for businesses - in fact, McKinsey estimates that "outages typically consume between a third and half of the overall maintenance budget and can reduce annual production volume by 5 to 10 percent." Can predictive maintenance turn the dial?

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Optimizing maintenance and shutdown planning with AI

Artificial intelligence provides critical insights for improved maintenance and shutdown planning, increasing the reliability of industrial facilities, and reducing operational costs.

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