Shifts recognition in correlated process data using a neural network

Chiu, Chien-Chao; Chen, Ming-Kuen; Lee, Ker-Ming

doi:10.1080/00207720120528

Cited by 32 publications

(9 citation statements)

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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%

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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Section: Applications Of Neural Network In Control Chartingmentioning

confidence: 99%

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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“…The basic assumption of SPC charts is that the processing datasets are independently and identically distributed (IID) about the process mean (Montgomery 2001). However, the process datasets collected from most continuous and batch process operations are usually serially correlated (Alwan and Roberts 1988;Chiu et al 2001Chiu et al , 2003Zhang 1998). It has been found that the performance of traditional control charts is significantly affected by autocorrelated data (Altiok and Melamed 2001;Alwan 1992;Harris and Ross 1991;Woodall and Faltin 1993;Zhang 1998).…”

Section: Introductionmentioning

confidence: 97%

An independent component analysis-based disturbance separation scheme for statistical process monitoring

2010

J Intell Manuf

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In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.

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“…Guh (2010) proposed a model using neural networks that simultaneously controls mean chart and range chart. Chiu, Chen, and Lee (2001) used BP and AR(1) time series model to design a perceptron network to recognise the changes that occur in parametric values of process. The results of their study show that neural networks have been very successful in differentiating the rate of changes based on standard deviation, while traditional control charts are unable to recognise the same process changes.…”

Section: Introductionmentioning

confidence: 99%

Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples

Lesany

Koochakzadeh

Ghomi

2013

International Journal of Production Research

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The correct, prompt recognition and analysis of unnatural and significant patterns in Schewhart's control charts are very important since they remind out-of-control conditions. In fact, pattern extraction increases the sensitivity of charts when identifying out of control conditions. Artificial neural networks have been used to identify unnatural patterns in many research studies due to their high efficiency in pattern recognition. In most of such studies, there is a significant risk of misclassification of highly sensitive patterns. To put it more clearly, the proposed models offered for the recognition of patterns with low parametric coefficients are mistaken. This study, offers a model for the recognition and analysis of basic patterns in process control charts using LVQ and MLP networks along with a fitted line analysis. In this model, not only does risk of misclassification at different levels of sensitivity decrease remarkably, but there will also be the possibility for recognition and analysis when basic pattern occur simultaneously. The efficiency and effectiveness of the model are shown by conducting tests based on simulation.

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Shifts recognition in correlated process data using a neural network

Cited by 32 publications

References 0 publications

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

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

An independent component analysis-based disturbance separation scheme for statistical process monitoring

Recognition and classification of single and concurrent unnatural patterns in control charts via neural networks and fitted line of samples

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