Estimation and generation of training patterns for control chart pattern recognition

Gutiérrez, Héctor De la Torre; Pham, Duc Truong

doi:10.1016/j.cie.2016.02.016

Cited by 31 publications

(3 citation statements)

References 51 publications

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“…An important aspect to pay attention to when developing CCPR models is that they must be general, i.e., able to identify a wide variety of patterns. The generation of training patterns to ensure generality of the CCPR model and to create benchmarks for comparing recognition accuracies is an issue studied by De la Torre Gutiérrez & Pham (2016Pham ( , 2018 who developed PGSs with that specific aim. The PGS proposed for autocorrelated patterns was the one adopted in this work.…”

Section: Resultsmentioning

confidence: 99%

A control chart pattern recognition system for feedback-control processes

Torre-Gutiérrez¹,

Pham

2019

Expert Systems with Applications

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Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Resultsmentioning

confidence: 99%

A control chart pattern recognition system for feedback-control processes

Torre-Gutiérrez¹,

Pham

2019

Expert Systems with Applications

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“…The researchers have also developed control chart pattern recognition methods for the feedback control process (De la Torre-Gutiérrez and Pham, 2019) as well as for the SPC-EPC process (Shao and Chiu, 2016). De la Torre Gutierrez and Pham (2016) proposed a new scheme comprising of change point detection and mean change categorization for estimating abnormal pattern parameters. Haghighati and Hassan (2018) studied CCPR with missing data and the effectiveness of restoring the patterns using exponential smoothing.…”

Section: Literature Reviewmentioning

confidence: 99%

A control chart pattern recognition methodology for controlling information technology-enabled service (ITeS) process customer complaints

John

2021

IJPPM

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PurposeThe purpose of this paper is to develop a control chart pattern recognition methodology for monitoring the weekly customer complaints of outsourced information technology-enabled service (ITeS) processes.Design/methodology/approachA two-step methodology is used to classify the processes as having natural or unnatural variation based on past 20 weeks' customer complaints. The step one is to simulate data on various control chart patterns namely natural variation, upward shift, upward trend, etc. Then a deep learning neural network model consisting of two dense layers is developed to classify the patterns as of natural or unnatural variation.FindingsThe validation of the methodology on telecom vertical processes has correctly detected unnatural variations in two terminated processes. The implementation of the methodology on banking and financial vertical processes has detected unnatural variation in one of the processes. This helped the company management to take remedial actions, renegotiate the deal and get it renewed for another period.Practical implicationsThis study provides valuable information on controlling information technology-enabled processes using pattern recognition methodology. The methodology gives a lot of flexibility to managers to monitor multiple processes collectively and avoids the manual plotting and interpretation of control charts.Originality/valueThe application of control chart pattern recognition methodology for monitoring service industry processes are rare. This is an application of the methodology for controlling information technology-enabled processes. This study also demonstrates the usefulness of deep learning techniques for process control.

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“…Since 1920s, statistical process control (SPC) theory has played an important role in product quality improvement and quality supervision [1]. SPC mainly uses a statistical analysis method to monitor the production process, and scientifically distinguishes the random fluctuation and abnormal fluctuation of product quality in the production process [2,3]. Thus, the abnormal trend of production process is expected, so that production managers can take timely measures to eliminate abnormalities and restore the stability of the process, so as to achieve the purpose of improving and controlling the quality.…”

Section: Introductionmentioning

confidence: 99%

Statistical Process Control with Intelligence Based on the Deep Learning Model

Zan

Liu

et al. 2019

Applied Sciences

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Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

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Estimation and generation of training patterns for control chart pattern recognition

Cited by 31 publications

References 51 publications

A control chart pattern recognition system for feedback-control processes

A control chart pattern recognition system for feedback-control processes

A control chart pattern recognition methodology for controlling information technology-enabled service (ITeS) process customer complaints

Statistical Process Control with Intelligence Based on the Deep Learning Model

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