Research Papers

Fault Diagnosis in Multistation Assembly Systems Using Spatially Correlated Bayesian Learning Algorithm

[+] Author and Article Information
Kaveh Bastani

Unifund LLC,
Cincinnati, OH 45242

Babak Barazandeh

Department of Industrial and
Systems Engineering,
University of Southern California,
Los Angeles, CA 90007

Zhenyu (James) Kong

Grado Department of Industrial and
Systems Engineering,
Virginia Polytechnic Institute and
State University,
Blacksburg, VA 24061
e-mail: zkong@vt.edu

1Corresponding author.

Manuscript received April 9, 2017; final manuscript received September 17, 2017; published online December 21, 2017. Assoc. Editor: Satish Bukkapatnam.

J. Manuf. Sci. Eng 140(3), 031003 (Dec 21, 2017) (10 pages) Paper No: MANU-17-1238; doi: 10.1115/1.4038184 History: Received April 09, 2017; Revised September 17, 2017

The problem of fault diagnosis for dimensional integrity in multistation assembly systems is addressed in this paper. Fault diagnosis under this context is to identify the process errors which significantly contribute to the large product dimensional variation based on sensor data. The main challenges to be resolved in this paper include (1) the number of measurements is less than the process errors, which is typical in practice, but results in an ill-posed estimation problem, and (2) there exists spatial correlation among the dimensional variation of process errors, which has not been addressed yet by existing literature. A spatially correlated Bayesian learning (SCBL) algorithm to address these challenges is developed. The SCBL algorithm is based on the relevance vector machine (RVM) by exploiting the spatial correlation of dimensional variation from various process errors, which occurs in some circumstances of assembled parts and is well defined in GD&T standards. The proposed algorithm relies on a parametrized prior including the spatial correlation, and eventually leads sparsity in fault diagnosis; hence, the issues with ill-posedness and structured process errors will be addressed. A number of simulation studies are performed to illustrate the superiority of SCBL algorithm over state-of-the-art algorithms in sparse estimation problems when spatial correlation exists among the nonzero elements. A real autobody assembly process is also used to demonstrate the effectiveness of proposed SCBL algorithm.

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Fig. 1

A three-part assembly process is presented. Fixture locators represented by Pij's are used to hold the parts during the operations.

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Fig. 2

Multiple feature composite control frame

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Fig. 4

The performance comparison of SCBL, BLASSO, and LASSO are presented in normalized mean squared error (MSE). Different correlation structures and different ill-posedness ratio (n/m), are considered in our comparisons. The results are based on 500 replications.

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Fig. 3

SCBL algorithm for mean shift fault diagnosis

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Fig. 5

Floor-pan assembly in three assembly stations

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Fig. 6

Fault diagnosis performance of SCBL, BLASSO, and LASSO are presented in normalized MSE. Different correlation coefficients varying in the range of (0, 0.9] are considered in the comparisons.



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