Asset health
A health score and condition (healthy, warning, at risk, critical) per asset, with plant and line roll-ups.
Predictive Maintenance turns machine data into a health score, anomaly detection, failure predictions and maintenance recommendations, and feeds the outcome of each maintenance action back into the next prediction.
The question it answers: What is happening with my machine, what will happen next, and what should I do?
A health score and condition (healthy, warning, at risk, critical) per asset, with plant and line roll-ups.
Abnormal behaviour is detected and recorded with the evidence behind it.
Predicted failure mode, probability and time window for each at-risk asset.
Remaining-life estimates, with a confidence interval.
A fleet-wide view of assets by probability of failure and asset criticality.
Recommendations become maintenance work orders that people can carry out and track.
Health before and after an action, and the outcome of each prediction, are kept to improve later predictions.
A machine with no history still gets rule-based predictions; as history builds up, statistical and machine-learning models take over.
Screens from a ProMonitor demonstration environment that uses simulated machine data. Select a screen to enlarge it.
assets/promonitor/pdm-overview.webp
assets/promonitor/pdm-asset-health.webp
assets/promonitor/pdm-risk-matrix.webp
Assets at risk are visible before they fail, so work can be scheduled instead of forced.
Recommendations, work orders and their results are connected in one place.
Predictions carry explanations so maintenance teams can judge them.
Talk to our team about machine connectivity, Industry 4.0 implementation and ProMonitor.