A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process

Huda, S, Abdollahian, M, Mammadov, M, Yearwood, J, Ahmad, S and Sultan, I 2014, 'A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process', European Journal of Operational Research, vol. 237, no. 3, pp. 857-870.


Document type: Journal Article
Collection: Journal Articles

Title A hybrid wrapper-filter approach to detect the source(s) of out-of-control signals in multivariate manufacturing process
Author(s) Huda, S
Abdollahian, M
Mammadov, M
Yearwood, J
Ahmad, S
Sultan, I
Year 2014
Journal name European Journal of Operational Research
Volume number 237
Issue number 3
Start page 857
End page 870
Total pages 14
Publisher Elsevier
Abstract With modern data-acquisition equipment and on-line computers used during production, it is now common to monitor several correlated quality characteristics simultaneously in multivariate processes. Multivariate control charts (MCC) are important tools for monitoring multivariate processes. One difficulty encountered with multivariate control charts is the identification of the variable or group of variables that cause an out-of-control signal. Expert knowledge either in combination with wrapper-based supervised classifier or a pre-filter with wrapper are the standard approaches to detect the sources of out-of-control signal. However gathering expert knowledge in source identification is costly and may introduce human error. Individual univariate control charts (UCC) and decomposition of statistics are also used in many cases simultaneously to identify the sources, but these either ignore the correlations between the sources or may take more time with the increase of dimensions. The aim of this paper is to develop a source identification approach that does not need any expert-knowledge and can detect out-of-control signal in less computational complexity. We propose, a hybrid wrapper–filter based source identification approach that hybridizes a Mutual Information (MI) based Maximum Relevance (MR) filter ranking heuristic with an Artificial Neural Network (ANN) based wrapper. The Artificial Neural Network Input Gain Measurement Approximation (ANNIGMA) has been combined with MR (MR-ANNIGMA) to utilize the knowledge about the intrinsic pattern of the quality characteristics computed by the filter for directing the wrapper search process. To compute optimal ANNIGMA score, we also propose a Global MR-ANNIGMA using non-functional relationship between variables which is independent of the derivative of the objective function and has a potential to overcome the local optimization problem of ANN training. The novelty of the proposed approaches is that they combine the advantages of both filter and wrapper approaches and do not require any expert knowledge about the sources of the out-of-control signals. Heuristic score based subset generation process also reduces the search space into polynomial growth which in turns reduces computational time. The proposed approaches were tested by exhaustive experiments using both simulated and real manufacturing data and compared to existing methods including independent filter, wrapper and Multivariate EWMA (MEWMA) methods. The results indicate that the proposed approaches can identify the sources of out-of-control signals more accurately than existing approaches.
Subject Applied Statistics
Keyword(s) Multivariate control chart
Fault diagnosis
Global optimization
Wrapper and filter approaches
DOI - identifier 10.1016/j.ejor.2014.02.032
Copyright notice © 2014 Elsevier B.V. All rights reserved.
ISSN 0377-2217
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Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
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