Variational inference-based transfer learning for profile monitoring with incomplete data

Abstract

Profile monitoring is a widely used tool in quality control. The rapid development of sensor technology has created unprecedented opportunities for multi-channel profile data collection, which motivates the modeling and transfer learning for multi-profile data. The Multi-output Gaussian Process (MGP) is often used for multi-profile data, due to its flexible modeling capability and elegant mathematical properties. However, two practical concerns limit the broader application of MGP for transfer learning and monitoring of multi-profile data: high computational complexity and data incompleteness. In this article, we propose a Variational Inference (VI)-based MGP framework to facilitate transfer learning and profile monitoring using incomplete profile data. The proposed framework features a specially designed convolutional structure for constructing an explicit covariance relationship between the inducing variables in VI and the MGP in multi-profile data. This structure inspires a comprehensive solution to both computational complexity and data incompleteness in modeling multi-profile data, which facilities the transfer learning for profile monitoring. Various numerical studies and one real case study are conducted to demonstrate and compare the transfer learning and monitoring performance of the proposed method. The results show the method can achieve superior monitoring performance while maintain a very low level of computational complexity.

Keywords:

• Incomplete data
• manufacturing systems
• profile monitoring
• transfer learning
• variational inference

Data availability statement

The code that reproduces Figure 4 and the data in case study are provided in supplementary materials.

Additional information

Notes on contributors

Amirhossein Fallahdizcheh

Amirhossein Fallahdizcheh is a data scientist in BorgWarner Inc. He received his BS degree from Iran University of Science and Technology in 2019 in industrial engineering, and his PhD from the University of Iowa in industrial and systems engineering in 2023. His research interests are statistical modelling, predictive analysis, and transfer learning. 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 BS 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. His research is supported by various federal funding agencies such as NSF, DoD, DoE, and DoT. He is a recipient of Outstanding Young Manufacturing Engineer Award from SME, Best Paper Award from IISE Transactions, and several Best Paper Awards/Finalist at INFORMS Annual Conferences. He is an Associate Editor of the Journal of Intelligent Manufacturing, and a member of INFORMS, IISE, and SME.