Anomaly detection for yield improvement in glass production

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

Abstract

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.

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Keywords

Smart Manufacturing System, Defect Prediction Technology, Defect Reduction

References
PREDICTRONICS CORP. Training Materials (November 29, 2017) Using Process Data for Quality Improvement

Ruoyu Li, David He, Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification, IEEE Transactions on Instrumentation and Measurement 61 (2012), 990-1001
Section
Special Session Papers