Improved SPC chart pattern recognition using statistical features

Hassan, Adnan; Baksh, Mohd Shariff Nabi; Shaharoun, Awaluddin M.; Jamaluddin, Hishamuddin

doi:10.1080/0020754021000049844

Cited by 149 publications

(94 citation statements)

References 14 publications

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“…Yang et al (2001) described two types of model-based tools for diagnosis of failures: in one statistical knowledge is used, while in the other analytical knowledge is utilized. In work discussed by Hassan et al (2003) existing tools for monitoring and diagnosis of problems have been improved using statistical features. Thissen et al (2001) considered the multi-variable statistical process of diagnosis of failures using neural network.…”

Section: Methodsmentioning

confidence: 99%

“…The nonlinear relationships between the various causes and the problems they create for production lead to increased complexity. Recently, several tools have been developed to address a variety of questions and problems related with CFQM (Pau, 1981;Yang et al, 2001;Dooley, & Kapoor, 1990;Debao, 2011;Hassan et al, 2003;Thissen et al, 2001;Bocaniala et al, 2006). Yang et al (2001) described two types of model-based tools for diagnosis of failures: in one statistical knowledge is used, while in the other analytical knowledge is utilized.…”

Section: Methodsmentioning

confidence: 99%

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Cross-Functional Quality Management: Case Study of Sowing of Spring Plants

Krueger¹,

Kurtener²,

Migut³

2016

EAJ

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Recently Cross-functional Quality Management (CFQM) increased intensively. However, the applications of CFQM in agriculture are not much. This article describes an approach for examination of job of several producer groups using the fuzzy indicator methodology, and its application for evaluation of sowing of spring wheat is proposed. The main advantage of the developed approach is the ease with which the condition attributes defined by producer groups can be estimated by expert panel.Keywords: cross-functional quality management, fuzzy indicator, sowing of spring plants This work is licensed under a Creative Commons Attribution 3.0 License.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Cross-Functional Quality Management: Case Study of Sowing of Spring Plants

Krueger¹,

Kurtener²,

Migut³

2016

EAJ

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“…A method called histogram representation was employed. Hassan et al [7] used ANNs for pattern recognition of SPC charts and compared two methods of data input with the ANNs: raw data and statistical features of data. The ANN with the statistical features performed better than the one with raw data.…”

Section: Introductionmentioning

confidence: 99%

Detecting flow meter drift by using artificial neural networks

Salamah¹,

Palaneeswaran²,

Savsar³

et al. 2011

Int. J. SDP

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In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meters used in industrial water supply. These fl ow meters are susceptible to drift, a condition causing them to give erroneous readings that are inconsistent with the actual fl ow. A simulation of industrial water fl ow to the industrial consumers was made. This simulation contained both healthy and drifting fl ow meter readings. ANN was built and trained on the simulated data. At the time of testing, the ANN developed was correct 89.52% of the time in determining the status of the fl ow recorded by a fl ow meter. Keywords: artifi cial neural network, fl ow meter drift, industrial water supply, statistical process control. INTRODUCTIONThe importance of water cannot be overemphasized. Because of this, people realized the importance of controlling, organizing, and regulating its use. No sensible controlling, organizing, regulating, and planning can be done without accurate measurement. Hence, the abundance of fl ow meters in any water system. Accurate measurement arrangements are required for effectively managing water infrastructure regimes. Flow meters can be subject to specifi c failures such as erroneous readings and breakdown. Inaccurate measurements affect the controlling, organizing, and regulating processes of the water system and planning. Measures for avoiding and mitigating such fl ow meter problems include: calibration, repair, and replacement. Flow meter calibration generally involves checking against accurate standards to determine any appreciable deviations and correcting for errors. Yet, this calibration is not a one time transaction as it does not end the problem forever.Flow meter calibration is often neglected by the operation and maintenance staff of a water system. Drift is an error in measurement, which can increase with time. Ben Salamah et al.[1] outlined a statistical process control (SPC)-based fl ow meter drift detection method for the industrial water use regime. Realizing the usefulness of artifi cial neural network (ANN) modeling for forecasting and classifi cation objectives, an extension of this research considered specifi c applications of ANN modeling with improved SPC frameworks for fl ow meters. This paper presents some key fi ndings from the application of ANN models for detecting fl ow meter drifts.

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“…In paper [9], authors have discussed effectiveness of measures like Skewness and correlation for feature selection in a pattern recognition task dealing with statistical process control data. In paper [10], authors have discussed a feature selection technique based on clustering the coefficient of variations.…”

Section: Related Workmentioning

confidence: 99%

An Univariate Feature Elimination Strategy for Clustering Based on Metafeatures

Goswami¹,

Chakraborty²,

Saha³

2017

IJISA

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Abstract-Feature selection plays a very important role in all pattern recognition tasks. It has several benefits in terms of reduced data collection effort, better interpretability of the models and reduced model building and execution time. A lot of problems in feature selection have been shown to be NP -Hard. There has been significant research in feature selection in last three decades. However, the problem of feature selection for clustering is still quite an open area. The main reason is unavailability of target variable as compared to supervised tasks. In this paper, five properties or metafeatures like entropy, skewness, kurtosis, coefficient of variation and average correlation of the features have been studied and analysed. An extensive study has been conducted over 21 publicly available datasets, to evaluate viability of feature elimination strategy based on the values of the metafeatures for feature selection in clustering. A strategy to select the most appropriate metafeatures for a particular dataset has also been outlined. The results indicate that the performance decrease is not statistically significant.

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Improved SPC chart pattern recognition using statistical features

Cited by 149 publications

References 14 publications

Cross-Functional Quality Management: Case Study of Sowing of Spring Plants

Cross-Functional Quality Management: Case Study of Sowing of Spring Plants

Detecting flow meter drift by using artificial neural networks

An Univariate Feature Elimination Strategy for Clustering Based on Metafeatures

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