Research Papers

Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis

[+] Author and Article Information
Zhenyu Kong

School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74078

Dariusz Ceglarek

Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL UK and Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI 53706

Wenzhen Huang

Department of Mechanical Engineering, University of Massachusetts, Dartmouth, MA 02747

J. Manuf. Sci. Eng 130(1), 011014 (Feb 15, 2008) (10 pages) doi:10.1115/1.2783228 History: Received October 04, 2006; Revised July 23, 2007; Published February 15, 2008

Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process-related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrating multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relationship between process-related error sources denoted by key control characteristics (KCCs) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 6

Procedure of the proposed fault diagnosis method: (a) Original measurement data, (b) hyper ellipse structure of the covariance matrix, (c) orthogonal diagonalization for the hyper ellipse, and (d) identification of the variations of the faults

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Figure 7

Illustration of the difference between the proposed method and DCA method

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Figure 8

An example of multistation assembly process

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Figure 9

Final assembled product through multistation assembly process

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Figure 1

Procedure of the proposed method

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Figure 2

Diagram of a multistation assembly process

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Figure 3

A schematic diagram of 3-2-1 fixture layout

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Figure 4

General 3-2-1 fixture layout

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Figure 5

3D assembly with part-to-part mating error



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