In-profile monitoring for cluster-correlated data in advanced manufacturing system

Abstract

Nowadays advanced sensing technology enables real-time data collection of key variables during manufacturing, known as multi-channel profiles. These data facilitate in-process monitoring and anomaly detection, which have been extensively studied in recent years. However, most studies treat each profile as a whole, e.g., a high-dimensional vector or function, and construct monitoring schemes accordingly. As a result, these methods cannot be implemented until the entire profile has been obtained, leading to long detection delay especially if anomalies occur in early sensing points of the profile. In addition, they require that profiles of different samples have the same time length and feature location, yet additional time-warping operation for real misaligned samples may weaken the anomaly patterns. To address these problems, this article proposes an in-profile monitoring (INPOM) control chart, which not only gives the feasibility of detecting anomalies inside the profile, but also can handle the misalignment problem of different samples. In particular, our INPOM scheme is built upon state space model (SSM). To better describe the clustered between-profile correlation and avoid overfitting, SSM is extended to a regularized SSM (RSSM), where regularizations are imposed as prior information and expectation maximization algorithm is integrated for posterior maximization to efficiently learn the model parameters. Furthermore, a monitoring statistic based on one-step-ahead prediction error of RSSM is constructed for INPOM control chart. Thorough numerical studies and real case studies demonstrate the effectiveness and applicability of our proposed RSSM-INPOM framework.

Keywords:

• between-profile correlation
• cluster-correlated data
• in-profile monitoring
• multi-channel profiles
• regularized state space model

Acknowledgments

We thank the editor and two anonymous referees for their valuable comments and suggestions for improving our work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are openly available in Mendeley Data at https://data.mendeley.com/datasets/sfv654hbnd/1.

Additional information

Funding

This work was supported by the NSFC Grant 71901131, 71932006, 72001139, 71731008, the BNSF Grant 9222014, the ASFC Grant 2020Z063058001, the SASTIND Grant JSZL2021205A001, and the Tsinghua University Intelligent Logistics & Supply Chain Research Center Grant THUCSL20182911756-001.

Notes on contributors

Peiyao Liu

Peiyao Liu is a PhD student in the Department of Industrial Engineering at Tsinghua University. Her email is [email protected].

Juan Du

Juan Du is an Assistant Professor in the Smart Manufacturing Thrust and an Affiliate Assistant Professor in the Department of Mechanical and Aerospace Engineering at The Hong Kong University of Science and Technology. Her email is [email protected].

Yangyang Zang

Yangyang Zang is an engineer at China Aero Polytechnology Establishment. She received her PhD in the Department of Industrial Engineering from Tsinghua University. Her email is [email protected].

Chen Zhang

Chen Zhang is an Associate Professor in the Department of Industrial Engineering at Tsinghua University. Her email is [email protected].

Kaibo Wang

Kaibo Wang is a Professor in the Department of Industrial Engineering at Tsinghua University. His email is [email protected].