The Next Level in Real-Time
AI-Based Predictive Maintenance
Conventional predictive maintenance detects failures after degradation patterns become statistically visible. ACI™ is designed to estimate the actual physical parameters governing a system in real time, with the potential to detect degradations at their onset, before they manifest as detectable patterns.
ACI™-based predictive maintenance is designed to:
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Detect degradations that conventional solutions cannot sense
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Recommend operational efficiency tactics based on real-time parameter estimates
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Schedule maintenance based on actual system condition, not fixed intervals
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Require no new sensors for currently controlled systems
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Train on synthetic data, requiring no failure histories
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Work with existing legacy systems and stream results to dashboards
Parameter-Based Detection vs. Pattern Recognition
The performance advantage of ACI™-based predictive maintenance stems from a fundamental difference in approach.
Conventional AI-based predictive maintenance primarily relies on vibration data, detecting degradation by recognizing patterns that correlate with known failure modes. This approach is bounded by the failure histories used to train it and cannot anticipate failure modes it has not been trained to recognize.
ACI™ is designed to estimate the actual physical parameters governing a system in real time, from its existing sensor data. Degradations are identified as parameter deviations from their nominal values, not as matches to previously seen vibration signatures. This means ACI™ is designed to detect any physically observable degradation, whether previously encountered or not, without requiring failure history data or additional sensors.
