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Multiple Fault Diagnosis Method in Multi-Station Assembly Processes Using State Space Model and Orthogonal Diagonalization Analysis

[+] Author Affiliations
Zhenyu Kong, Ramesh Kumar, Suren Gogineni, Yingqing Zhou

Dimensional Control Systems, Inc.

Jijun Lin

Massachusetts Institute of Technology

Wenzhen Huang

University of Massachusetts

Dariusz Ceglarek

University of Wisconsin

Paper No. IMECE2005-80340, pp. 1201-1212; 12 pages
doi:10.1115/IMECE2005-80340
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Manufacturing Engineering and Materials Handling, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Manufacturing Engineering Division and Materials Handling Division
  • ISBN: 0-7918-4223-1 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

Dimensional control has a significant impact on the overall product quality and performance in large and complex multi-station assembly systems. From measurement data, the way to identify root causes for large variation of Key Product Characteristics (KPCs) is one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multi-station assembly process by integrating multivariate statistical analysis with engineering model. Based on product/process information, by using the state space model, a set of fault patterns for multi-station assembly process are developed, which explicitly represent the relationship between the error sources and KPCs. The vectors of these patterns form an affine system. Afterwards, the Principal Component Analysis (PCA) is applied to conduct orthogonal diagonalization of the measurement data. Thus, the measurement data can be easily projected to the axes of the affine system. Whereby, the significance of each fault pattern shall be estimated accurately. Finally, a few case studies are also provided to validate the proposed methodology.

Copyright © 2005 by ASME

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