A study on the various features for effective control chart pattern recognition

Gauri, Susanta Kumar; Chakraborty, Shankar

doi:10.1007/s00170-006-0591-6

Cited by 46 publications

(26 citation statements)

References 17 publications

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“…The nine features chosen from [4] are AASBP, ABDPE, SRANGE, ALSPI, ASL, RVE, BRANGE, SASPE and DABL. Among them AASBP, ASL, SRANGE, ALSPI and ABDPE were found to be dependent on process standard deviation, and hence a modified form is used (see Appendix 1).…”

Section: Methodsmentioning

confidence: 99%

“…Among the nine statistical features chosen from [4], some of them have been modified here in order to make them independent of process standard deviation. It has been found that some of these features depend on shifts in operating characteristics of CCPs, which is taken into consideration in this study.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to developing the recognizer, a comparative analysis is performed between statistical feature-based input and frequency sample (namely, discrete cosine transform (DCT))-based input to the SVM classifier. In order to build an effective feature-based recognizer, some new statistical features along with some optimal features taken from [4] are also suggested. The overall approach of this work is stated below:…”

Section: Plan Of Workmentioning

confidence: 99%

“…These extracted features were then fed to the recognizer, which was heuristic, induction or neural network based. In another work [4], the authors proposed thirty-two measures based on statistical properties, which were used in classification and regression tree analysis to find out thirteen optimal features and to create a decision rule for the detector. However, the relative importance of features, in their paper, reflects a different picture.…”

Section: Statistical Feature Basedmentioning

confidence: 99%

“…Following are the mathematical formulations of the features used in this work, all of which have been taken from [4] with some minor modifications, in case of SRANGE, RVE and ABDPE.…”

Section: Appendix 1: Formulation Of Featuresmentioning

confidence: 99%

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An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

Das

Banerjee

2010

Neural Comput & Applic

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Early detection of unnatural control chart patterns (CCP) is desirable for any industrial process. Most of recent CCP recognition works are on statistical feature extraction and artificial neural network (ANN)-based recognizers. In this paper, a two-stage hybrid detection system has been proposed using support vector machine (SVM) with self-organized maps. Direct Cosine transform of the CCP data is taken as input. Simulation results show significant improvement over conventional recognizers, with reduced detection window length. An analogous recognition system consisting of statistical feature vector input to the SVM classifier is further developed for comparison.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Plan Of Workmentioning

confidence: 99%

Section: Statistical Feature Basedmentioning

confidence: 99%

“…Following are the mathematical formulations of the features used in this work, all of which have been taken from [4] with some minor modifications, in case of SRANGE, RVE and ABDPE.…”

Section: Appendix 1: Formulation Of Featuresmentioning

confidence: 99%

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An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

Das

Banerjee

2010

Neural Comput & Applic

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The use of neural networks in statistical process control charts

Psarakis

2011

Quality & Reliability Eng

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Neural networks (NNs) are massively parallel computing mechanism emulating a human brain. It has been proved that they had a satisfactory performance when they were used for a wide variety of applications. In the recent years, the efficiencies that provided the NNs also began to be applied in statistical process control (SPC). SPC charts have become one of the most commonly used tools for monitoring process stability and variability in today's manufacturing environment. These tools are used to determine whether the process is statistically under or out of control but in some cases such as the presence of autocorrelation as well as the presence of a specific pattern in the data do not provide the possibility of correctly and quickly detecting and classifying the existing fault. These problems have led many researchers to propose alternative methods for monitoring processes such as the use of NNs. In this paper, we discuss issues concerning the combination of both tools. Specifically, we study the NNs for the detection and determination of mean and/or variance shifts as well as in pattern recognition in the SPC charts. Furthermore, the use of NNs when the data are correlated is discussed. Finally, the use of NNs in multivariate control charts is addressed. The networks architectures that were used for each case, the way of operation and the performance of the proposed NNs applications are pointed out.

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Improved feature-based test statistic for assessing suitability of the preliminary samples for constructing control limits of $$ \bar{X} $$ chart

Gauri

2011

Int J Adv Manuf Technol

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A study on the various features for effective control chart pattern recognition

Cited by 46 publications

References 17 publications

An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

An hybrid detection system of control chart patterns using cascaded SVM and neural network–based detector

The use of neural networks in statistical process control charts

Improved feature-based test statistic for assessing suitability of the preliminary samples for constructing control limits of $$ \bar{X} $$ chart

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