Redefining Fault Detection with Neural Networks
Redefining Fault Detection with Neural Networks
Central utility plants generate massive volumes of operational data, but traditional fault detection methods rely on static thresholds and rule-based alarms that miss subtle performance degradation.
This session explores how neural network models can transform fault detection and diagnostics in chilled water and heating systems by learning normal operating patterns and identifying anomalies that conventional approaches overlook. Attendees will see how machine learning applied to real plant data catches equipment drift, sensor failures, and efficiency losses earlier, reducing unplanned downtime and extending asset life.
The presentation draws on field results from live facility deployments to show what neural network fault detection looks like in practice, not in theory.
- Neural networks learn what “normal” looks like for your specific plant, so they catch subtle performance drift that fixed threshold alarms miss entirely.
- Early anomaly detection on real operational data reduces unplanned downtime and extends equipment life by flagging sensor failures, efficiency losses, and mechanical degradation before they escalate.
- Machine learning fault detection works today in live facility environments, not just in research settings, and the results show measurable improvements in maintenance response time and energy performance.
