Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

Boran, Semra; Diren, Deniz Demircioglu

doi:10.12693/aphyspola.132.1054

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…Trending charts have become an indispensable tool in almost every field in research and industry with special focus on the healthcare industry such as in hospitals, medical device manufacturing and pharmaceutical firms [14][15][16][17]. However, the ease of implementation without compromising the conclusion deduced from Shewhart charts is the crux of their application [18,19].…”

Section: Discussionmentioning

confidence: 99%

Application of control charts for non-normally distributed data using statistical software program: A technical case study

Mostafa¹

2019

World J. Adv. Res. Rev.

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Control charts are a valuable assessment tool in the healthcare industry. The ease of use of these trending charts is crucial to obtain timely important results with minimum time and efforts. The current case showed analysis of nonnormal data to obtain control charts with useful output without using exhaustive different means of transformation and/or omitting aberrant numbers. Raw results for the quality of purified water from water treatment plant that converts municipal water to purified water were collected from two points-of-use -(ζ and Ψ). Data gathered were conductivity and total organic carbon (TOC) measurements. The statistical processing and control charts were done using commercial statistical software package. Statistical analysis of data showed that conductivity and TOC results of both points did not follow Gaussian distribution except TOC of point Ψ where it passed normality test, but they were closest to other distributions. There were several observations of outlier values from the results. Moreover, data normalization did not improve after removal of the extreme values. Data were switched to be interpreted using Laneymodified attribute control charts and compared with the original results drawn using individual-moving range (I-MR). Interestingly, both types of control charts agreed regarding control limits and some alarm points. I-MR and Laneyattribute charts could be used for non-normal data with unusual other types of distributions that may not be suitable for conventional types of control charts with the variable charts possess greater sensitivity of alarm detection over the attribute charts.

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Section: Discussionmentioning

confidence: 99%

Application of control charts for non-normally distributed data using statistical software program: A technical case study

Mostafa¹

2019

World J. Adv. Res. Rev.

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“…The result shows that TDNN outperforms the other three techniques in classification accuracy. Boran and Diren (2017) proposed a model by forming an individual control chart for every variable and determining out-of-control conditions for every control chart. Then the multilayer NN model is to be developed based on the values of individual control charts.…”

Section: Neural Network and Graph Theorymentioning

confidence: 99%

“…Recently, research on the application of NN to multivariate process control is conducted by Bersimis et al (2021), Shao and Lin (2019), and Boran and Diren (2017). Bersimis et al (2021) introduced a meta-method that combines the results of four well-known analytical methods.…”

Section: Neural Network and Graph Theorymentioning

confidence: 99%

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Design multivariate statistical process control procedure in the case of Ethio cement

Tegegne

Azene

Berhan

2022

IJQRM

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PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.

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“…Hence, we consider that the detection of the secondary oscillations or at least finding a relationship between disorders and the secondary oscillation patterns in higher-order Fourier domains can help the early diagnosis of some cardiac disorders. Indeed, the machine learning (ML) algorithms use the features of data to more accurately discriminate them into disordered or non-disordered classes (Boran and Diren 2017;Recioui et al 2017;Edla et al 2016), whereas finding a unique feature which has different values for distinct disorders and non-disorders' cases can be beneficial for such ML algorithms as well as increasing their accuracies in the discrimination of data (Yüksel et al 2017;Cömert and Kocamaz 2017;Ceylan et al 2016;Adar et al 2015;Dekhandji 2017).…”

Section: Introductionmentioning

confidence: 99%

Detection of abnormalities in heart rate using multiple Fourier transforms

Erkuş

Purutçuoğlu

2019

Int. J. Environ. Sci. Technol.

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Fourier transform (FT) is one of the transformation techniques to convert the time-domain signal into the frequency-domain signal. Due to its easy usage, it is applied in many engineering approaches where the data are made of periodic components. Electrocardiography (ECG) is an imaging modality which represents the data of electrophysiological activities of heart. ECG data are gathered from the electrodes that are placed on the specific locations on chest, and electrical activities of heart generally produce periodically shaped time series data. However, this periodicity can be perturbed, or side oscillations can occur due to certain abnormal activities in hearts. From our preliminary studies, we have found that the implementation of the FT multiple times in ECG datasets can be useful in the detection of main and hidden periodicities in autonomous applications. Especially when they are hard to be observed with the first FT. Hereby, this study aims to improve the accuracy of successful detection of such perturbations in ECG data by applying multiple FT and finding the relationship between the side oscillations and also detection of some features of the n th FT in datasets of various cardiac disorders

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Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

Cited by 5 publications

References 21 publications

Application of control charts for non-normally distributed data using statistical software program: A technical case study

Application of control charts for non-normally distributed data using statistical software program: A technical case study

Design multivariate statistical process control procedure in the case of Ethio cement

Detection of abnormalities in heart rate using multiple Fourier transforms

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