US 11,782,429 B2
Automatically adapting a prognostic-surveillance system to account for age-related changes in monitored assets
Richard P. Sonderegger, Dorchester, MA (US); Kenneth P. Baclawski, Waltham, MA (US); Guang C. Wang, San Diego, CA (US); Anna Chystiakova, Palo Alto, CA (US); Dieter Gawlick, Palo Alto, CA (US); Zhen Hua Liu, San Mateo, CA (US); and Kenny C. Gross, Escondido, CA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Jul. 7, 2021, as Appl. No. 17/368,840.
Prior Publication US 2023/0008658 A1, Jan. 12, 2023
Int. Cl. G05B 23/02 (2006.01); G06N 20/00 (2019.01); G05B 15/02 (2006.01); G05B 13/02 (2006.01)
CPC G05B 23/0221 (2013.01) [G05B 13/0265 (2013.01); G05B 15/02 (2013.01); G06N 20/00 (2019.01); G05B 2223/02 (2018.08)] 19 Claims
OG exemplary drawing
 
1. A method for automatically adapting a prognostic-surveillance system to account for aging phenomena in a monitored system, the prognostic-surveillance system comprising one or more machine-learning models, the method comprising:
receiving time-series signals associated with measurements obtained at one or more sensors in the monitored system;
analyzing, using a trained inferential machine-learning model, the time-series signals to detect incipient anomalies associated with the monitored system;
periodically determining a reward/cost metric associated with using an additional trained inferential machine-learning model trained to account for aging phenomena in the monitored system;
responsive to determining that the reward/cost metric exceeds a threshold, using the additional trained inferential machine-learning model to account for one or more aging phenomena in the monitored system;
using the additional trained inferential machine-learning model in the prognostic-surveillance system;
detecting, by the prognostic-surveillance system and using the additional trained inferential learning model, an incipient anomaly in the monitored system; and
in response to the detecting, performing a servicing operation on the monitored system to remediate the incipient anomaly.