Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

Cook, Deborah F.; Chiu, Chih‐Chou

doi:10.1080/07408179808966453

Cited by 59 publications

(33 citation statements)

References 21 publications

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“…Recently, machine learning algorithms have been proposed for use within SPC (see, e.g., Chinnam 2002;Cook and Chiu 1998;Deng et al 2012;Hwang et al 2007;Sun and Tsung 2003). Although all of these methods are not presented in a big data setting, we discuss them since they may be scalable to high volume, high velocity data.…”

Section: Labeled Datamentioning

confidence: 99%

Statistical Perspectives on “Big Data”

Megahed

Jones‐Farmer

2015

Frontiers in Statistical Quality Control 11

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As our information infrastructure evolves, our ability to store, extract, and analyze data is rapidly changing. Big data is a popular term that is used to describe the large, diverse, complex and/or longitudinal datasets generated from a variety of instruments, sensors and/or computer-based transactions. The term big data refers not only to the size or volume of data, but also to the variety of data and the velocity or speed of data accrual. As the volume, variety, and velocity of data increase, our existing analytical methodologies are stretched to new limits. These changes pose new opportunities for researchers in statistical methodology, including those interested in surveillance and statistical process control methods. Although it is well documented that harnessing big data to make better decisions can serve as a basis for innovative solutions in industry, healthcare, and science, these solutions can be found more easily with sound statistical methodologies. In this paper, we discuss several big data applications to highlight the opportunities and challenges for applied statisticians interested in surveillance and statistical process control. Our goal is to bring the research issues into better focus and encourage methodological developments for big data analysis in these areas.

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Section: Labeled Datamentioning

confidence: 99%

Statistical Perspectives on “Big Data”

Megahed

Jones‐Farmer

2015

Frontiers in Statistical Quality Control 11

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“…Interested readers are referred to Heykin 23 33 have used neural networks as an effective approach to control pattern recognition assuming independent observations. However, a few researchers, including Cook and Chiu 4 and Chiu et al 34 , have used neural networks to study the impact of autocorrelated observations on the performance of control charts. Cook and Chiu 4 use an autoregressive model of order one, AR (1), to model observations in a papermaking data set from Pandit and Wu 35 and a viscosity data set from Box and Jenkins 36 .…”

Section: Applications Of Neural Network In Control Chartingmentioning

confidence: 99%

“…Figure 3 shows a time series in which non-random disturbances are introduced at times t = 7, 27, 81 and 92. These non-random disturbances are detected successfully using the neural network procedure proposed by Cook and Chiu 4 . However, their procedure is not equipped to classify different types of non-random shocks.…”

Section: Applications Of Neural Network In Control Chartingmentioning

confidence: 99%

“…This point will be discussed in the next section. Cook and Chiu 4 consider different numbers of hidden nodes to train their network and based on their results they determine the optimum network for identifying non-random behaviors of observations. One advantage of their method is its reasonable speed to detect non-random disturbances.…”

Section: Applications Of Neural Network In Control Chartingmentioning

confidence: 99%

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Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes

Noorossana

Farrokhi

Saghaei

2003

Quality & Reliability Eng

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This paper presents an artificial neural network model for detecting and classifying three types of non-random disturbances referred to as level shift, additive outlier and innovational outlier which are common in autocorrelated processes. To the best of our knowledge, this is the first time that a neural network has been considered for simultaneous detection and classification of such non-random disturbances. An AR (1) model is considered to characterize the quality characteristic of interest in a continuous process where autocorrelated observations are generated over time. The performance of the proposed procedure is evaluated through the use of a numerical example. Preliminary results indicate that the procedure can be used effectively to detect and classify unusual shocks in autocorrelated processes.

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“…Therefore, other feedforward neural networks for quality control have been proposed in the literature. For example, Cook and Chiu (1998), in order to recognise mean shifts in autocorrelated manufacturing process parameters, proposed a radial basis function (RBF) neural network system.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing quality control by means of a Fuzzy ART network trained on natural process data

Pacella

Semeraro

Anglani

2004

Engineering Applications of Artificial Intelligence

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In order to produce products with constant quality, manufacturing systems need to be monitored for any unnatural deviations in the state of the process. Control charts have an important role in solving quality control problems; nevertheless, their effectiveness is strictly dependent on statistical assumptions that in real industrial applications are frequently violated. In contrast, neural networks can elaborate huge amounts of noisy data in real time, requiring no hypothesis on statistical distribution of monitored measurements. This important feature makes neural networks potential tools that can be used to improve data analysis in manufacturing quality control applications. In this paper, a neural network system, which is based on an unsupervised training phase, is presented for quality control. In particular, the adaptive resonance theory (ART) has been investigated in order to implement a model-free quality control system, which can be exploited for recognising changes in the state of a manufacturing process. The aim of this research is to analyse the performances of ART neural network under the assumption that predictable unnatural patterns are not available. To such aim, a simplified Fuzzy ART neural algorithm is firstly discussed, and then studied by means of extensive Monte Carlo simulation. r

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Using radial basis function neural networks to recognize shifts in correlated manufacturing process parameters

Cited by 59 publications

References 21 publications

Statistical Perspectives on “Big Data”

Statistical Perspectives on “Big Data”

Using Neural Networks to Detect and Classify Out‐of‐control Signals in Autocorrelated Processes

Manufacturing quality control by means of a Fuzzy ART network trained on natural process data

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