Recognizing Mixture Control Chart Patterns with Independent Component Analysis and Support Vector Machine

Lu, Chi-Jie; Shao, Yuehjen E.; Li, Po-Hsun; Wang, Yu-Chiun

doi:10.1007/978-3-642-13318-3_53

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Classifying mixture patterns is a difficult and challenging task. There are very few papers in the literature which discuss identification of concurrent patterns [10,13,14,15,16,22,25]. Accurate identification of concurrent patterns helps in correctly recognizes if the concentration of dissolved parameter is increasing or decreasing over time.…”

Section: Introductionmentioning

confidence: 99%

Identification of Concurrent Control Chart Patterns in Time Series

Sharma¹,

Swayne²,

Obimbo³

2015

IJIRSET

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Control chart patterns (CCPs) can be associated with certain assignable causes, and recognition of such patterns can assist the diagnostic search for those causes. Variations could be one or more instances of trend, cyclic, hugging, sudden shift or some other variations over time. Each pattern has special statistical characteristics which differentiate one pattern from another. In a time series, presence of more than one pattern may exist and identification of concurrent pattern is important. In this paper, we will utilize a new approach, RobustICA, for identification of concurrent patterns which is efficient when compared to traditional approaches being used for feature extraction. It will identify independent components hidden in mixture patterns and input those independent components to decision trees for recognition of as many as eight separate control chart patterns.

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Section: Introductionmentioning

confidence: 99%

Identification of Concurrent Control Chart Patterns in Time Series

Sharma¹,

Swayne²,

Obimbo³

2015

IJIRSET

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Determining the state of a process control system: Current trends and future challenges

Shardt

Zhao

et al. 2011

Can J Chem Eng

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In many industrial plants, multiple, interconnected control loops are common. Their maintenance and improvement requires detailed controller performance assessment to determine not only whether they are behaving well, but also to determine the potential cause of any observed problems. Techniques for performance assessment can be divided into two broad categories (1) performance assessment of regulatory control loops; and (2) performance assessment of supervisory control loops that evaluate the economic performance of advanced control strategies, such as model predictive control (MPC). A comprehensive review of the literature on the industrial applications of performance assessment, as well as some of the currently available software, is also presented. © 2011 Canadian Society for Chemical Engineering

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Applying ICA and SVM to Mixture Control Chart Patterns Recognition in a Process

Shao

Chang

2011

Advances in Neural Networks – ISNN 2011

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Recognizing Mixture Control Chart Patterns with Independent Component Analysis and Support Vector Machine

Cited by 5 publications

References 6 publications

Identification of Concurrent Control Chart Patterns in Time Series

Identification of Concurrent Control Chart Patterns in Time Series

Determining the state of a process control system: Current trends and future challenges

Applying ICA and SVM to Mixture Control Chart Patterns Recognition in a Process

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