Optimum Detection Capability with Low False Alert Rates
Humaware’s Adaptive Anomaly Detector provides users with trusted actionable information to optimise maintenance schedules and increase asset availability.
The Adaptive Anomaly Detector identifies deviations as anomalies; these include events, discontinuities and trends in the data. Early detection of these types of anomalies is essential for providing enough time to plan and perform maintenance operations during the estimated remaining useful life of the asset.
it is an essential building block for implementing predictive maintenance.
The Adaptive Anomaly Detector offers:
Low false alert rates
Optimum detection capability
No setting & Maintaining individual fixed thresholds
Reliable data for remaining useful life estimates
Not machine or asset specific
Use any time series data
A Solution You Can Depend Upon
Humaware’s Adaptive Anomaly Detector allows you to create competitive, effective and scalable Condition Monitoring and Predictive Maintenance solutions.
Standalone
Solution
The Adaptive Anomaly Detector is available as a stand-alone solution. APIs allow you to interface with the cloud-based Adaptive Anomaly Detector and view the results in your own interface.
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Extensively
Proven
Humaware’s technology has been extensively proven in the rail and aerospace sectors and can be applied to any application which requires the analysis of time series
data.
Meeting Predictive Maintenance Needs
Accurate and early detections are are essential for the implementation of predictive/prescriptive maintenance and the generation of remaining useful life
estimates.
End-to-End
Solution
SmartVision™ by EKE-Electronics is powered by Humaware’s Adaptive Anomaly Detector to provide a multi-fleet remote diagnostics & predictive condition-based maintenance system for train and track
Visit SmartVision™Solve Your Threshold Management Problems
The application of Condition Monitoring/Predictive Maintenance to a large population (or fleets) of assets often results in reduced effectiveness of your current solution. Typical issues include:
HIGH NUMBER OF MISSED ALERTS
DATA VARIATION ACROSS ASSETS
DEPENDENCIES ON OPERATING CONDITIONS
DATA VARIABILITY/STABILITY
HIGH FALSE ALARM RATE
HIGH DATA MANAGEMENT
Many feel that they are left with no choice but to apply individually tailored fixed thresholds for each individual asset in a population. This may not be a problem for you right now, but as your solution scales, the task of setting and managing individual fixed thresholds across hundreds of assets can quickly become unmanageable, distracting valuable resource and extending the time to detect failures.