Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble

Yang, Wen-An; Zhou, Wei

doi:10.1007/s10845-013-0847-6

Cited by 34 publications

(18 citation statements)

References 40 publications

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“…For example, some have employed various NNs to recognize CCPs for processes [8,9]. A NN ensemble-enabled model was used to classify unnatural CCPs for an autocorrelated process [10]. Process observations are assumed to follow an autoregressive with order 1 (i.e., AR(1)) process with an unknown constant coefficient.…”

Section: Related Workmentioning

confidence: 99%

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Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Shao

Chang

2017

Complexity

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The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.

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Section: Related Workmentioning

confidence: 99%

“…Because autocorrelation widely exists in practical chemical or continuous processes [10,11,[25][26][27], the autocorrelation structure should be included in the process model. Therefore, the EPC is usually employed to compensate for effects of the autocorrelation and disturbances.…”

Section: The Industrial Process Modelsmentioning

confidence: 99%

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Shao

Chang

2017

Complexity

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“…Since the early 1990s, numerous CCPR classifiers based on ANNs have been proposed. The most significant works include learning vector quantization (LVQ) networks (Pham and Oztemel 1994;Guh 2008;Gauri 2010;Gu et al 2013;Yang and Zhou 2013), multilayer perceptron (MLP) networks (Cheng 1997;Pham and Wani 1997;Guh and Tannock 1999;Al-Assaf 2004;Chen et al 2007;Pingyu et al 2009;Ranaee and Ebrahimzadeh 2013;Masood and Hassan 2013) and probability neural network (PNN) (Cheng and Ma 2008). Additionally, Ahmadzadeh et al (2013) applied neural networks to identify the process parameter change of multivariate exponentially weighted moving average (MEWMA) control charts and achieve quality improvement at reduced overall cost.…”

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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“…Although the approach is able to overcome the correlation problems for SPC applications, the use of EPC may conceal the effects of underlying process disturbances. These embedded disturbance effects imply that process personnel have more difficulty recognizing underlying CCPs [28].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

Shao

2020

Mathematics

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A statistical process control (SPC) chart is one of the most important techniques for monitoring a process. Typically, a certain root cause or a disturbance in a process would result in the presence of a systematic control chart pattern (CCP). Consequently, the effective recognition of CCPs has received considerable attention in recent years for their potential use in improving process quality. However, most studies have focused on the recognition of CCPs for SPC applications alone. Specifically, even though numerous studies have addressed the increased use of the SPC and engineering process control (EPC) mechanisms, very little research has discussed the recognition of CCPs for multiple-input multiple-output (MIMO) systems. It is much more difficult to recognize the CCPs of an MIMO system since two or more disturbances are simultaneously involved in the process. The purpose of this study is thus to propose several machine learning (ML) classifiers to overcome the difficulties in recognizing CCPs in MIMO systems. Because of their efficient and fast algorithms and effective classification performance, the considered ML classifiers include an artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and multivariate adaptive regression splines (MARS). Furthermore, one problem may arise due to the existence of embedded mixture CCPs (MCCPs) in MIMO systems. In contrast to using typical process outputs alone in a classifier, this study employs both process outputs and EPC compensation to ensure the effectiveness of CCP recognition. Experimental results reveal that the proposed classifiers are able to effectively recognize MCCPs for MIMO systems.

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Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble

Cited by 34 publications

References 40 publications

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

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

Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process

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