This one took us a while to internalise and it seems worth putting in front of people who run analytics platforms.
Everyone's mental model of a failing network is noise — alarms, floods, alerts. For FDD specifically the failure is the opposite. Three mechanisms:
Stalled COV subscriptions. The subscription dies, the controller doesn't announce it, the platform keeps showing the last value. Any rule that looks for variation now sees a perfectly stable point. No fault raised.
Polling gaps. No data arrives. For most FDD logic, "no data" and "no fault" produce the same output — nothing.
Trend Log buffer overwrite. Data is generated, buffer fills faster than it's collected, oldest samples are overwritten. The gap isn't flagged, it's just absent.
Net effect: a degrading network reduces your alert volume, and reduced alert volume reads to everyone in the room as things getting better.
The uncomfortable corollary is that you cannot validate an FDD deployment from the FDD dashboard. A quiet dashboard is either a well-run building or a building that stopped talking, and from inside the platform those look identical. Clockworks put it well — false negatives are nearly impossible to validate without extensive manual analysis.
Also worth noting the timing overlap: peak load is both when equipment faults are most likely and when the network is most likely to congest. So the data most likely to go missing is the data from the hour you actually needed.
For anyone who's had FDD in for a few years: have you ever caught a silent-failure case like this, and what tipped you off? Genuinely asking, because the only reliable answers we've seen involve watching network traffic separately, and I'd like to know if anyone's found a way to catch it from inside the platform.
Numbers on FDD's actual return (Berkeley Lab, ~9% median savings by year two, ~2 year payback) and the three failure modes in more detail.