Anomaly detection for yield improvement in glass production



Published Sep 4, 2023
Haruo Yonemori Kenichi Arai Hironobu Yamamichi Ichiro Sakata Makoto Imamura


Predictive maintenance using manufacturing sensor data has attracted attention for reducing defects and selecting appropriate actions. This paper proposes an anomaly detection method using lasso regression and group-wise variable selection based on FTA (Fault Tree Analysis) domain knowledge. We evaluated our approach using real factory data and found that its precision and false positive rate are 66% and 30%, respectively. Moreover, we validate that the visualization of the contribution rate for anomaly detection is helpful for factory maintenance.

Abstract 108 | PDF Downloads 138



Smart Manufacturing System, Defect Prediction Technology, Defect Reduction

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