Rule-based FDD signatures like simultaneous heating/cooling and economizer faults, rule-based versus statistical/ML anomaly detection, common AHU and VAV fault libraries, triaging alerts without alarm fatigue, and quantifying a fault's dollar cost to justify repair.
This module works through how a rule-based FDD platform encodes known failure signatures — simultaneous heating and cooling, a stuck economizer damper, a valve that never reaches the position it was commanded to — and where those transparent, explainable rules run out, handing off to statistical and machine-learning methods built to catch failure modes nobody thought to write a rule for. Standing AHU and VAV fault libraries, alert triage that avoids alarm fatigue, and a worked cost-quantification example are all covered with the same practical grounding this program applies throughout.
By the end of this module you should be able to explain why a single bad sensor can cascade into a dozen nuisance alarms, and translate a detected fault into an annual dollar figure a facilities manager can actually act on rather than defer.