US 12,216,686 B1
Machine learning technologies for predicting results of cable fire tests
Christopher Hauman, Mount Prospect, IL (US); Anthony Tassone, Melville, NY (US); and Jiyuan Kang, Highland Park, IL (US)
Assigned to UL LLC, Northbrook, IL (US)
Filed by UL LLC, Northbrook, IL (US)
Filed on Jan. 19, 2024, as Appl. No. 18/417,490.
Int. Cl. G06F 16/28 (2019.01)
CPC G06F 16/285 (2019.01) 11 Claims
OG exemplary drawing
 
1. A computer-implemented method for predicting an outcome of a large-scale product test, the computer-implemented method comprising:
receiving, by one or more processors, a set of small-scale results of a product tested according to a small-scale product test representative of the large-scale product test;
calculating, by the one or more processors and based on the set of small-scale results as a first input to a first machine learning model of a plurality of machine learning models, a first result predicting an outcome of the product tested according to the large-scale product test, wherein calculating the first result includes:
determining, by the one or more processors, a first classification for the product, and
calculating, by the one or more processors, a confidence value for the first classification;
calculating, by the one or more processors and based on the set of small-scale results as a second input to at least one second machine learning model of the plurality of machine learning models, a second result predicting the outcome of the product tested according to the large-scale product test, wherein calculating the second result includes:
determining, by the one or more processors and based on a test profile for the large-scale product test, a second classification for the product, wherein determining the second classification includes:
predicting, by the one or more processors and based on the set of small-scale results as the second input to the at least one second machine learning model of the plurality of machine learning models, the test profile, wherein:
the at least one second machine learning model includes a plurality of regression models, and
each regression model of the plurality of regression models predicts a test value of a plurality of test values of the test profile, the plurality of test values including at least one of: (i) a flame spread value; (ii) a total heat release value; (iii) a peak heat release rate value; and (iv) a fire growth rate index value; and
predicting, by the one or more processors, an outcome of the large-scale product test based at least on the first result and the second result.