Profile monitoring based on transfer learning of multiple profiles with incomplete samples

Abstract

Profile monitoring is an important tool for quality control. Most existing profile monitoring approaches focus on monitoring a single profile. In practice, multiple profiles also widely exist and these profiles contain rich correlation information that can benefit the monitoring of interested/target profile. In this article, we propose a transfer learning framework to extract profile-to-profile inter-relationship to improve the monitoring performance. In this framework, profiles are modeled as a multi-output Gaussian process (MGP), and a specially designed covariance structure is proposed to reduce the computational load in optimizing the MGP parameters. More importantly, the proposed framework contains features for dealing with incomplete samples in each profile, which facilitates the information sharing among profiles with different data collection costs/availability. The proposed method is validated and compared with various benchmarks in extensive numerical studies and a case study of monitoring ice machine temperature profiles. The results show the proposed method can successfully transfer knowledge from related profiles to benefit the monitoring performance in the target profile. The R code of this paper would be available as on-line supplementary materials.

Keywords:

• Transfer learning
• profile monitoring
• incomplete data
• manufacturing systems

Acknowledgments

The authors thank the editors and reviewers for their valuable comments and suggestions.

Additional information

Funding

This research is supported by the Reducing EMbodied-Energy And Decreasing Emissions (REMADE) Institute Project 20-01-RR-4038.

Notes on contributors

Amirhossein Fallahdizcheh

Amirhossein Fallahdizcheh is a PhD Student in the Department of Industrial and System Engineering in University of Iowa. His research interests are statistical modelling, predictive analysis, and transfer learning. Prior to his PhD studies, he received his BS degree from Iran University of Science and Technology in 2019 in Industrial Engineering. He is a member of INFORMS and IISE.

Chao Wang

Chao Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Iowa. He received his B.S. from the Hefei University of Technology in 2012, and MS from the University of Science and Technology of China in 2015, both in Mechanical Engineering, and his MS in Statistics and PhD in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2018 and 2019, respectively. His research interests include statistical modeling, analysis, monitoring and control for complex systems. He is member of INFORMS, IISE, and SME.