US 11,928,971 B2
Detection of anomalous states in multivariate data
Nigel Stepp, Santa Monica, CA (US); Tsai-Ching Lu, Thousand Oaks, CA (US); and Franz David Betz, Renton, WA (US)
Assigned to BOEING COMPANY, Arlington, VA (US)
Filed by THE BOEING COMPANY, Chicago, IL (US)
Filed on Dec. 16, 2021, as Appl. No. 17/644,700.
Claims priority of provisional application 63/169,402, filed on Apr. 1, 2021.
Prior Publication US 2022/0327943 A1, Oct. 13, 2022
Int. Cl. G08G 5/00 (2006.01); G07C 5/00 (2006.01); G07C 5/08 (2006.01)
CPC G08G 5/0043 (2013.01) [G07C 5/008 (2013.01); G07C 5/0816 (2013.01); G07C 5/085 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining, at one or more processors, a plurality of data sets, each data set of the plurality of data sets including multivariate time series data for a respective sample period of a plurality of sample periods;
for each data set of the plurality of data sets, determining, by the one or more processors, recurrence data indicative of recurrent states in the data set;
for each data set of the plurality of data sets, determining, by the one or more processors based on the recurrence data, determinism values of a determinism metric and laminarity values of a laminarity metric;
determining, by the one or more processors, that a particular data set of the plurality of data sets includes data representing an anomalous state based on a determinism-laminarity curve representing the particular data set, wherein the determinism-laminarity curve is based on the determinism values of the particular data set and the laminarity values of the particular data set; and
generating, by the one or more processors, output data indicating the anomalous state.