Monitoring and Performance

Pretrained models are validated against asset-specific historical and live data, with a focus on operational relevance rather than purely statistical accuracy. We evaluate how well detected anomalies correlate with known failure modes, degradation patterns, and maintenance events. This ensures the model highlights actionable abnormal states, not just outliers.

Validated models are adapted to the asset’s operating profile, environmental conditions, and usage patterns. This includes aligning features, thresholds, and sensitivities with real-world constraints and acceptable variations. The result is reliable detection that fits day-to-day operations without alert fatigue.

Models are continuously retrained using real-time data and confirmed operational feedback. This allows the system to evolve with wear, process changes, and seasonal or load-related effects. Over time, detection accuracy improves while maintaining transparency, traceability, and trust in the results.