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Predictive Maintenance
Hexagon’s solutions for predictive maintenance help avoid manufacturing equipment downtime utilising AI and machine learning to detecting anomalies and interpret warning signs before downtime events occur enabling manufacturers to accelerate remedial actions.
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Hexagon’s Predictive Maintenance solution uses advanced analytics, AI and machine learning to proactively predict and respond to issues before they occur in real time.
In most organisations, the operational model is reactive to quality and production issues. If a component fails a quality measurement, a process alert is issued, the event is logged, products are scrapped and a quality manager or production manager diagnoses the machine issue.
With Predictive Maintenance, manufacturers get live-stream data gathering, in-loop data analytics and real-time monitoring for signals that are precursors to downtime events, meaning that issues can be detected and remediated quickly. By eliminating downtime events, the solution offers up to 20% reduction in scrap.
In most organisations, the operational model is reactive to quality and production issues. If a component fails a quality measurement, a process alert is issued, the event is logged, products are scrapped and a quality manager or production manager diagnoses the machine issue.
With Predictive Maintenance, manufacturers get live-stream data gathering, in-loop data analytics and real-time monitoring for signals that are precursors to downtime events, meaning that issues can be detected and remediated quickly. By eliminating downtime events, the solution offers up to 20% reduction in scrap.
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Features & benefits
- Measurement data is analysed and predicted
- Out-of-spec situations can be predicted based on repeated measurements on artifacts if needed
- Cloud and on-premise ready
- Service activity history and prediction, Error and downtime root cause analyses
- Process capability evaluation based on neural nets as available in Q-DAS qs-STAT
- Measurement data is analysed and predicted
- Out-of-spec situations can be predicted based on repeated measurements on artifacts if needed
- Cloud and on-premise ready
- Service activity history and prediction, Error and downtime root cause analyses
- Process capability evaluation based on neural nets as available in Q-DAS qs-STAT
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