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.