2016
DOI: 10.1016/j.cie.2015.12.017
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Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Abstract: International audienceControl charts are among the main tools in statistical process control (SPC) and have been extensively used for monitoring industrial processes. Currently, besides the single control charts, there is an interest in the concurrent ones. These graphics are characterized by the simultaneous presence of two or more single control charts. As a consequence, the individual patterns may be mixed, hindering the identification of a non-random pattern acting in the process; this phenomenon is refere… Show more

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Cited by 25 publications
(15 citation statements)
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“…The input of MLP becomes CCP feature set, which includes statistical features (mean, standard deviation, skewness and kurtosis) and shape features (S, NC1, NC2, APML and APSL). These features are designed by experts in this field, and have been proved to be very effective in many years of application [18,19,[33][34][35]. Therefore, the first input layer of MLP has nine neurons, which are used to receive the above nine features respectively.…”
Section: Comparison Of Ccpr Results Of Several Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The input of MLP becomes CCP feature set, which includes statistical features (mean, standard deviation, skewness and kurtosis) and shape features (S, NC1, NC2, APML and APSL). These features are designed by experts in this field, and have been proved to be very effective in many years of application [18,19,[33][34][35]. Therefore, the first input layer of MLP has nine neurons, which are used to receive the above nine features respectively.…”
Section: Comparison Of Ccpr Results Of Several Methodsmentioning
confidence: 99%
“…One form is to take the raw data as input [24,32,33], such as quality data in the control chart and the frequency of each interval in the histogram. Another form is to take the features extracted from the raw data as input, such as wavelet features [11], shape features [19,34,35] and statistical features [23,34]. The latter is called feature engineering, that is, experts design favorable features for pattern recognition problems based on experience.…”
Section: Introductionmentioning
confidence: 99%
“…They implemented and compared three different classifiers: Decision Tree, ANN, and the Self-adjusting Association Rules Generator (SARG) for process CCPs that were generated by predefined equations of GARH (Generalized Autoregressive Conditional Heteroskedasticity) Model for X̅ chart. Pelegrina et al used different Blind Source Separation (BSS) methods in the task of unmixing concurrent control charts to achieve high classification rates [12]. Gutierrez and Pham presented a new scheme to generate training patterns for ML algorithms: Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) [13].…”
Section: Production Trend Forecast Methodsmentioning
confidence: 99%
“…Blind source separation (BSS) deals with the problem of finding both the unknown input sources and unknown mixing system from only observed output mixtures. BSS has recently become the focus of intensive research work due to its high potential in many applications such as antenna processing, speech processing and pattern recognition [1][2][3]. The recent successes of BSS might be also used in mechanical engineering [4][5][6][7].…”
Section: Introductionmentioning
confidence: 99%