Identification of control chart patterns using wavelet filtering and robust fuzzy clustering |
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Authors: | Chih-Hsuan Wang Way Kuo |
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Affiliation: | (1) Department of Business Administration, Ming Chuan University, 250, Sec. 5, Chung Shan N. Rd., Taipei, 11103, Taiwan;(2) Department of Industrial and Information Engineering, University of Tennessee, Knoxville, TN, USA |
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Abstract: | This paper proposes a hybrid framework composed of filtering module and clustering module to identify six common types of
control chart patterns, including natural pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward
trend. In particular, a multi-scale wavelet filter is designed for denoising and its performance is compared to single-scale
filters, including mean filter and exponentially weighted moving average (EWMA) filter. Moreover, three fuzzy clustering algorithms,
based on fuzzy c means (FCM), entropy fuzzy c means (EFCM) and kernel fuzzy c means (KFCM), are adopted to compare their performance of pattern classification. Experimental results demonstrate that the
excellent performance of EFCM and KFCM against outliers, especially in the case of high noise level embedded in the input
data. Therefore, a hybrid framework combining wavelet filter with robust fuzzy clustering is suggested and proposed in this
paper. Compared to neural network based approaches, the proposed method provides a promising way for the on-line recognition
of control chart patterns because of its efficient computation and robustness against outliers. |
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Keywords: | Control chart Pattern recognition Wavelet denoising Robust fuzzy clustering Outlier |
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