US 12,436,528 B2
Building management system with supervisory fault detection layer
Mohammad N. Elbsat, Milwaukee, WI (US); Michael J. Wenzel, Oak Creek, WI (US); Jingduo Fan, Milwaukee, WI (US); Joshua D. Carl, Milwaukee, WI (US); and Robert D. Turney, Watertown, WI (US)
Assigned to TYCO FIRE & SECURITY GMBH, Neuhausen am Rheinfall (CH)
Filed by Johnson Controls Tyco IP Holdings LLP, Milwaukee, WI (US)
Filed on Jul. 29, 2021, as Appl. No. 17/389,085.
Claims priority of provisional application 63/058,695, filed on Jul. 30, 2020.
Prior Publication US 2022/0035357 A1, Feb. 3, 2022
Int. Cl. G05B 23/02 (2006.01); G05B 15/02 (2006.01); G06N 3/045 (2023.01); G06N 5/04 (2023.01)
CPC G05B 23/024 (2013.01) [G05B 15/02 (2013.01); G06N 3/045 (2023.01); G06N 5/04 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method for detecting faults in a building management system (BMS), the method comprising:
receiving time series data characterizing an operating performance of one or more BMS devices;
generating a first fault detection result by processing the time series data using a first fault detection technique;
generating a second fault detection result by processing the time series data using a second fault detection technique different than the first fault detection technique, wherein the second fault detection result conflicts with the first fault detection result;
applying both the first fault detection result and the second fault detection result as inputs to a neural network configured to output an indication of whether a fault condition is occurring in the BMS; and
initiating an action to resolve the fault condition in response to the indication indicating that the fault condition is occurring in the BMS,
wherein processing the time series data using the second fault detection technique comprises using a temporal detection method comprising:
determining an expected value of the time series data based on inferences made by a regression model;
calculating a residual value between an actual value of the time series data and the expected value of the time series data;
generating statistical inferences based on the residual value; and
providing the statistical inferences as the second fault detection result, and
wherein generating the statistical inferences based on the residual value comprises generating a cumulated sum (CUSUM) of the time series data or cumulated sum squared (CUSUMSQ) of the time series data or a recursive residual of the time series data.