Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Xie, Liangjun; Gu, Nong; Li, Dalong; Cao, Zhiqiang; Tan, Min; Nahavandi, Saeid

doi:10.1016/j.cie.2012.10.009

Cited by 31 publications

(9 citation statements)

References 38 publications

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“…In order to better exploit the application of BSS techniques to concurrent control charts, the experiments are divided in four parts. The control charts considered in each experiment were obtained from an automatic pattern generator based on the literature [7,9,10,11,13,17,18,21,24,25] and described in Table 1, 1 containing T = 100 samples each one. The experiments are presented in the next sections.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Such an approach allows one to identify concurrent charts but also to estimate which pattern provides the major contribution into the observed mixture. Gu et al [20] and Xie et al [21] applied singular spectrum analysis to decompose the concurrent patterns utilized learn vector quantization [20] and support vector machine [21] to recover the individual patterns. Some other approaches have applied the independent component analysis (ICA), a methodology that was developed to solve the problem of blind source separation (BSS) [22,23], to extract the independent components 6 (ICs) from the concurrent control charts.…”

Section: Introductionmentioning

confidence: 99%

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Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Pelegrina

Duarte

Jutten

2016

Computers & Industrial Engineering

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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 refered as concurrent charts. In view of this problem, our first goal is to investigate the importance of an efficient separation step for pattern recognition. Then, we compare the efficiency of different Blind Source Separation (BSS) methods in the task of unmixing concurrent control charts. Furthermore, these BSS methods are combined with shape and statistical features in order to verify the performance of each one in pattern classification. In additional, the robustness of the better approach is tested in scenarios where there are different non-randomness levels and in cases with imbalanced dataset provided to the classifier. After simulating different patterns and applying several separation methods, the results have shown that the recognition rate is widely influenced by the separation and feature extraction steps and that the selection of efficient separation methods is fundamental to achieve high classification rates

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Pelegrina

Duarte

Jutten

2016

Computers & Industrial Engineering

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“…Authors such as [7] and [30] achieved good pattern recognition accuracies with SVMs. Other authors such as [11,5] and [8] have utilised signal processing techniques such as Independent Component Analysis and Wavelet transforms to pre-process the control chart data, and also achieved good pattern recognition accuracies.…”

Section: Svm and Pnnmentioning

confidence: 99%

Estimation and generation of training patterns for control chart pattern recognition

Gutiérrez

Pham

2016

Computers & Industrial Engineering

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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“…• Singular spectrum analysis (SSA) and learning vector quantization network were applied by [5]. The authors also tested their methodology against real data acquired from aluminium smelting processes; • In [6], the authors presented the application of RobustICA along with a decision tree in order to recognize patterns from the extracted features; • Blind source separation methods were addressed by [7].…”

Section: Introductionmentioning

confidence: 99%

Automatic quality control systems based on pattern recognition and sparse signal processing

Palma¹,

Duarte²

2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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This paper presents the application of two methods for automatic detection and classification of abnormal quality control patterns. A generator of synthetic concurrent control charts was implemented to create mixtures from patterns described in the literature. In order to obtain features for the classification step, the generated charts were initially processed via sparse regression using the Least Absolute Shrinkage and Selection Operator (LASSO) method. Then, we assess the performance of the classifier which was founded on an artificial neural network (ANN) on two different situations: i) with inputs given by the observed (raw) data and ii) with inputs given by the features generated by the LASSO method. ANN fed with sparse inputs performed extremely close to the ANN fed with raw data, using considerably less inputs.

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Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Cited by 31 publications

References 38 publications

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Blind source separation and feature extraction in concurrent control charts pattern recognition: Novel analyses and a comparison of different methods

Estimation and generation of training patterns for control chart pattern recognition

Automatic quality control systems based on pattern recognition and sparse signal processing

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