US 11,915,112 B2
Method for classification based diagnosis with partial system model information
Ion Matei, Sunnyvale, CA (US); Johan de Kleer, Los Altos, CA (US); Alexander Feldman, Santa Cruz, CA (US); and Maksym Zhenirovskyy, Mountain View, CA (US)
Assigned to XEROX CORPORATION, Norwalk, CT (US)
Filed by Palo Alto Research Center Incorporated, Palo Alto, CA (US)
Filed on Sep. 3, 2019, as Appl. No. 16/559,069.
Prior Publication US 2021/0065065 A1, Mar. 4, 2021
Int. Cl. G06N 3/12 (2023.01); G06F 17/10 (2006.01); G06F 17/11 (2006.01); G06F 17/16 (2006.01); G06N 3/08 (2023.01); G06N 3/126 (2023.01); G06N 20/20 (2019.01)
CPC G06N 20/20 (2019.01) [G06F 17/10 (2013.01); G06F 17/11 (2013.01); G06F 17/16 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A device comprising:
at least one processor; and
at least one memory including computer program code;
the at least one memory and the computer program code configured to, with the at least one processor, cause the device to:
perform a classification-based diagnosis for at least one of detecting and predicting faults in a physical system using partial system model information, said partial system model information including at least one of a system topology and component behavior, wherein said classification-based diagnosis includes:
during an offline learning phase, learning parameters of an unknown component of the physical system, wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the device at least to:
learn the parameters of the unknown component by solving:

OG Complex Work Unit Math
where β is the parameters, j is a possible mode, and y0:T is an output of a system that includes the unknown component; and
during an online learning phase, predicting a current mode based on the learned parameters of the unknown component.