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BIGDAS2020
2020년 8권 1호 p.63 ~ p.66
Product Quality Prediction by Multivariate Timeseries Anomaly Detection
Product Quality Prediction by Multivariate Timeseries Anomaly Detection
Manas Bazarbaev
Hyoseok Oh , Aziz Nasridinov , Kwan-Hee Yoo
- Abstract -

ISSN : 2466-135X
2020.11.26-28
Busan, South Korea

Abstract. In this paper we were tried to predict product quality by number of founded anomalies in real time data from sensors. Initially we have real time data from sensors, and we made anomaly detection model for this data. Then we have product information data and from this data we are generating data with product quality and number of anomalies during producing of this product. Last, we are trying to make classification of product quality. The goal of our research is to understand is it possible to predict product quality from anomalies information.

- Key Words -
Anomaly detection, Machine learning, Sensors data, Product quality.
Anomaly detection, Machine learning, Sensors data, Product quality.
- 논문 구분 -
국외전문학술지
- 사사 정보 -
The work is supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under i-Ceramic manufacturing innovation platform technology development business. No.2004367, Development of cloud big data platform for the innovative manufacturing in ceramic industry and by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2020-0-01462) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)”
- Downloads -
[BIGDAS2020]Product Quality Prediction by Multivariate Timeseries Anomaly Detection.pdf