US 12,467,692 B2
Flame analytics system
David Kucera, Bilovice nad Svitavou (CZ); Curtis Taylor, Paradise Valley, AZ (US); Jonathan McDonald, Shakopee, MN (US); Gregory Stewart, North Vancouver (CA); and Wyatt Culler, Berne, IN (US)
Assigned to HONEYWELL INTERNATIONAL INC., Charlotte, NC (US)
Filed by Honeywell International Inc., Charlotte, NC (US)
Filed on Dec. 27, 2023, as Appl. No. 18/397,833.
Application 18/397,833 is a continuation of application No. 16/665,572, filed on Oct. 28, 2019, granted, now 11,898,800.
Claims priority of provisional application 62/755,297, filed on Nov. 2, 2018.
Prior Publication US 2024/0125554 A1, Apr. 18, 2024
Int. Cl. F23N 5/24 (2006.01); F23N 5/16 (2006.01); F27D 21/04 (2006.01); G06F 16/21 (2019.01); G06N 5/02 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); F27D 21/00 (2006.01); F27D 21/02 (2006.01)
CPC F27D 21/04 (2013.01) [F23N 5/16 (2013.01); F23N 5/242 (2013.01); G06F 16/219 (2019.01); G06N 5/02 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); F23N 2223/04 (2020.01); F23N 2223/08 (2020.01); F23N 2223/36 (2020.01); F23N 2223/48 (2020.01); F23N 2229/00 (2020.01); F23N 2241/04 (2020.01); F23N 2900/05006 (2013.01); F27D 2021/0057 (2013.01); F27D 2021/026 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A flame analytics system, comprising:
a burner;
one or more sensors at or about the burner, wherein the one or more sensors include two or more video sensors positioned at different angles with respect to a flame in the burner, and wherein the two or more video sensors are configured to capture video data pertaining to the flame, wherein the video data comprise 2D views of the flame;
a processor configured to:
reconstruct the 2D views of the flame into 3D views of the flame, wherein the 3D views of the flame are utilized to distinguish the flame from a background of the flame in the video data, and
determine one or more parameters associated with the flame based on the 3D views of the flame;
a historical database connected to the one or more sensors;
a model training module connected to the historical database, wherein a machine learning algorithm is trained based on the historical database; and
a runtime algorithm module connected to the one or more sensors and the model training module, wherein the runtime algorithm module compares real time data from the one or more sensors and the historical data from the model training module in accordance with the machine learning algorithm.