Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems

Swift, Jill A.; Mize, Joe H.

doi:10.1016/0360-8352(94)00028-l

Cited by 71 publications

(35 citation statements)

References 2 publications

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“…The existing methods in literatures can be classified into three categories: expert system-based approaches, statistical analysis approaches and machine learning-based approaches. As for expert system-based approaches, other important works have been further made to develop expert systems for control chart patterns recognition on the basic of Swift's work (Cheng and Hubele 1992;Swift and Mize 1995a;Bag et al 2012). He et al (2013) proposed a decision tree (DT) learning based model for bivariate process mean shift monitoring and fault identification.…”

Section: Literature Reviewmentioning

confidence: 99%

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

2015

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Accurate control chart patterns recognition (CCPR) plays an essential role in the implementation of control charts. However, it is a challenging problem since nonrandom control chart patterns (CCPs) are normally distorted by "common process variations". In this paper, a novel method of CCPR by integrating fuzzy support vector machine (SVM) with hybrid kernel function and genetic algorithm (GA) is proposed. Firstly, two shape features and two statistical features that do not depend on the distribution parameters and number of samples are presented to explicitly describe the characteristics of CCPs. Then, a novel multiclass method based on fuzzy SVM with a hybrid kernel function is proposed. In this method, the influence of outliers on classification accuracy of SVM-based classifiers is weakened by assigning a degree of membership for every training sample. Meanwhile, a hybrid kernel function combining Gaussian kernel and polynomial kernel is adopted to further enhance the generalization ability of the classifiers. To solve the issue of features selection and parameters optimization, GA is used to simultaneously optimize the input features subsets and parameters of fuzzy SVM-based classifier. Finally, several simulation experiments and a real example are addressed to validate the feasibility and effectiveness of the proposed methodology. And the results of simulation experiments demonstrate that it can achieve excellent performance for CCPR and outperforms other approaches, such as learning B Pingyu Jiang vector quantization network, multi-layer perceptron network, probability neural network, fuzzy clustering and SVM, in term of recognition accuracy. The results of the practical cases manifest that the proposed method has application potential for solving the problem of control chart interpretation in real-world.

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Section: Literature Reviewmentioning

confidence: 99%

Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function

2015

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“…Most of the research work on pattern recognition for control charts involves the application of either neural networks [9][10][11] or expert systems [12,13]. Neural network-based pattern recognisers consist of a number of interconnected processing elements that are usually called neurons.…”

Section: Pattern Recognitionmentioning

confidence: 99%

Pattern recognition for statistical process control charts

Wang¹,

Kochhar²,

Hannam³

1998

Int J Adv Manuf Technol

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“…Some researchers have used expert systems for the recognition of unnatural models [2,3]. The advantage of an expert system is that it contains information explicitly.…”

Section: Introductionmentioning

confidence: 99%