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A new approach to predictive maintenance, without false alarms based on pre-defined thresholds, that use up valuable resources.
Predictive maintenance is the holy grail for reliability engineers. However, rule-based predictive maintenance has its limitations, lacking the ability to detect new anomalies that can lead to sudden equipment failure. This whitepaper explores how predictive maintenance methods can be enhanced by machine learning (ML) and artificial intelligence (AI).
Who should read this document?
Interested in a demo of one of our data solution products?
DataHUB4.0 is our enterprise data historian solution, OPUS is our Auto AI platform and OASIS is our remote control solution for Smart Cities and Facilities.
Book your demo with our team today!
Ready to embark on a pilot project or roll-out AI innovation enterprise wide? Perhaps you need assistance integrating your systems or storing your big data? Whatever the situation, we are ready to help you on your digital transformation.
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.
Learn more about DataHUB4.0, VROC's distributed enterprise data historian. Complete the form form to download the product sheet.
Discover how you can connect disparate systems and smart innovations in one platform, and remotely control your smart facility. Complete the form to download the product sheet.
'OPUS, an artistic work, especially on a large scale'
Please complete the form to download the OPUS Product Sheet, and discover how you can scale Auto AI today.
Interested in reading the technical case studies? Complete the form and our team will be in touch with you.
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