US 12,321,163 B2
Prediction apparatus, prediction method, and program
Hiroshi Okamoto, Tokyo (JP); Marina Takahashi, Tokyo (JP); Shuji Shinohara, Tokyo (JP); Shunji Mitsuyoshi, Tokyo (JP); Masahiro Haitsuka, Tokyo (JP); Hidetoshi Kozono, Tokyo (JP); and Fumihiro Miyoshi, Tokyo (JP)
Assigned to DAICEL CORPORATION, Osaka (JP)
Appl. No. 17/797,343
Filed by DAICEL CORPORATIOn, Osaka (JP)
PCT Filed Feb. 4, 2021, PCT No. PCT/JP2021/004179
§ 371(c)(1), (2) Date Aug. 3, 2022,
PCT Pub. No. WO2021/157670, PCT Pub. Date Aug. 12, 2021.
Claims priority of application No. 2020-017474 (JP), filed on Feb. 4, 2020.
Prior Publication US 2023/0057291 A1, Feb. 23, 2023
Int. Cl. G05B 23/02 (2006.01)
CPC G05B 23/0264 (2013.01) [G05B 23/024 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A prediction apparatus that predicts a characteristic value of a product by using process data obtained from a production facility, the prediction apparatus comprising:
processing circuitry configured to:
read the process data from a storage device configured to store the process data obtained from the production facility; and
generate a prediction model on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data, the first process data and the second process data or the value corresponding to the second process data being included in the read process data, the first process data being used as a predetermined explanatory variable, the second process data or the value corresponding to the second process data being used as a response variable, the prediction model having learned features of the process data obtained from the production facility, wherein the prediction model for determining a positive/negative variation direction of the response variable is generated in accordance with a positive/negative variation direction of the explanatory variable by performing regression analysis using a penalty function that increases a penalty when the positive/negative variation direction of the response variable and the positive/negative variation direction of the explanatory variable are opposite to a positive/negative variation direction indicated by the causality information; and
calculate a predicted value by using the prediction model and to output the predicted value.