Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

[1]  Noorbakhsh Amiri Golilarz,et al.  Control chart pattern recognition using RBF neural network with new training algorithm and practical features. , 2018, ISA transactions.

[2]  Hassen Taleb,et al.  Support vector regression based residual control charts , 2010 .

[3]  Gian Antonio Susto,et al.  Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[4]  Jun Lv,et al.  Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines , 2013, Comput. Ind. Eng..

[5]  A. Ben Hamza,et al.  Statistical process control using kernel PCA , 2007, 2007 Mediterranean Conference on Control & Automation.

[6]  A. Ebrahimzadeh,et al.  Application of the PSO-SVM model for recognition of control chart patterns. , 2010, ISA transactions.

[7]  Li Li,et al.  On Fault Identification of MEWMA Control Charts Using Support Vector Machine Models , 2013 .

[8]  Murat Kulahci,et al.  The Effect of Autocorrelation on the Hotelling T2 Control Chart , 2015, Qual. Reliab. Eng. Int..

[9]  Ratna Babu Chinnam,et al.  General support vector representation machine for one-class classification of non-stationary classes , 2008, Pattern Recognit..

[10]  Geert Gins,et al.  Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis , 2017 .

[11]  Wenming Cheng,et al.  Features Fusion Exaction and KELM With Modified Grey Wolf Optimizer for Mixture Control Chart Patterns Recognition , 2020, IEEE Access.

[12]  Georg Carle,et al.  Application of Forecasting Techniques and Control Charts for Traffic Anomaly Detection , 2008 .

[13]  Zhihong Miao,et al.  Control Chart Pattern Recognition Based on Convolution Neural Network , 2018, Smart Innovations in Communication and Computational Sciences.

[14]  Kristof Mertens,et al.  B. DE KETELAERE ET AL. , 2011 .

[15]  Jill A. Swift,et al.  Out-of-control pattern recognition and analysis for quality control charts using LISP-based systems , 1995 .

[16]  Chunhui Zhao,et al.  Total Variable Decomposition Based on Sparse Cointegration Analysis for Distributed Monitoring of Nonstationary Industrial Processes , 2020, IEEE Transactions on Control Systems Technology.

[17]  Eugene Tuv,et al.  Learning patterns through artificial contrasts with application to process control , 2003 .

[18]  W. A. Shewhart,et al.  Some applications of statistical methods to the analysis of physical and engineering data , 1924 .

[19]  Antonio Fernando Branco Costa,et al.  The effect of the autocorrelation on the performance of the T2 chart , 2015, Eur. J. Oper. Res..

[20]  Min Zhang,et al.  Recognition of mixture control chart patterns based on fusion feature reduction and fireworks algorithm-optimized MSVM , 2018, Pattern Analysis and Applications.

[21]  Chuen-Sheng Cheng,et al.  Diagnosing the variance shifts signal in multivariate process control using ensemble classifiers , 2016 .

[22]  Charles W. Champ,et al.  A multivariate exponentially weighted moving average control chart , 1992 .

[23]  Jie Xu,et al.  Control Chart Pattern Recognition Method Based on Improved One-dimensional Convolutional Neural Network , 2019, IFAC-PapersOnLine.

[24]  Ruey-Shiang Guh,et al.  Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach , 2008 .

[25]  Junghui Chen,et al.  Wavelet functional principal component analysis for batch process monitoring , 2020 .

[26]  Zhenmin Cheng,et al.  Lumped Reaction Kinetic Models for Pyrolysis of Heavy Oil in the Presence of Supercritical Water , 2016 .

[27]  Wibawati,et al.  Comparing the performance of T 2 chart based on PCA Mix, Kernel PCA Mix, and Mixed Kernel PCA for Network Anomaly Detection , 2021 .

[28]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[29]  M. Ahsan,et al.  Multivariate control chart based on PCA mix for variable and attribute quality characteristics , 2018 .

[30]  Tai-Yue Wang,et al.  Mean shifts detection and classification in multivariate process: a neural-fuzzy approach , 2002, J. Intell. Manuf..

[31]  J.D.T. Tannock,et al.  Recognition of control chart concurrent patterns using a neural network approach , 1999 .

[32]  Edgard M. Maboudou-Tchao,et al.  Monitoring the mean vector with Mahalanobis kernels , 2018 .

[33]  Muhammad Mashuri,et al.  Multivariate Control Chart Based on Kernel PCA for Monitoring Mixed Variable and Attribute Quality Characteristics , 2020, Symmetry.

[34]  Yarema Okhrin,et al.  New Approaches for Monitoring Image Data , 2020, IEEE Transactions on Image Processing.

[35]  Chandrasegar Thirumalai,et al.  Prediction of diabetes disease using control chart and cost optimization-based decision , 2017, 2017 International Conference on Trends in Electronics and Informatics (ICEI).

[36]  Tobias Senst,et al.  Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[37]  Tai-Yue Wang,et al.  Artificial neural networks to classify mean shifts from multivariate χ2 chart signals , 2004, Comput. Ind. Eng..

[38]  Chuen-Sheng Cheng,et al.  Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines , 2008, Expert Syst. Appl..

[39]  Fu-Kwun Wang,et al.  One‐sided control chart based on support vector machines with differential evolution algorithm , 2019, Qual. Reliab. Eng. Int..

[40]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[41]  Jesús Silva,et al.  U-Control Chart Based Differential Evolution Clustering for Determining the Number of Cluster in k-Means , 2019, GPC.

[42]  Federico Castanedo,et al.  A Review of Data Fusion Techniques , 2013, TheScientificWorldJournal.

[43]  Liping Zhao,et al.  A Support Vector Machine Based Multi-kernel Method for Change Point Estimation on Control Chart , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[44]  Bin Chen,et al.  A new exponentially weighted moving average control chart for monitoring the coefficient of variation , 2014, Comput. Ind. Eng..

[45]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[46]  Fadel M. Megahed,et al.  Statistical Perspectives on “Big Data” , 2015 .

[47]  Germano Veiga,et al.  Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry , 2019, J. Intell. Manuf..

[48]  Seoung Bum Kim,et al.  Data mining model-based control charts for multivariate and autocorrelated processes , 2012, Expert Syst. Appl..

[49]  Adnan Hassan,et al.  Fuzzy Heuristics and Decision Tree for Classification of Statistical Feature-Based Control Chart Patterns , 2021, Symmetry.

[50]  Seoung Bum Kim,et al.  One-class classification-based control charts for multivariate process monitoring , 2009 .

[51]  Duc Truong Pham,et al.  Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks , 1993 .

[52]  Rita Peñabaena-Niebles,et al.  Support vector machine in statistical process monitoring: a methodological and analytical review , 2017 .

[53]  Talayeh Razzaghi,et al.  A cost-sensitive convolution neural network learning for control chart pattern recognition , 2020, Expert Syst. Appl..

[54]  Klemens Böhm,et al.  Active Learning of SVDD Hyperparameter Values , 2019, 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA).

[55]  Jaime A. Camelio,et al.  A Review and Perspective on Control Charting with Image Data , 2011 .

[56]  Petros Xanthopoulos,et al.  A weighted support vector machine method for control chart pattern recognition , 2014, Comput. Ind. Eng..

[57]  Jialin Liu,et al.  Nonstationary fault detection and diagnosis for multimode processes , 2009 .

[58]  Ratna Babu Chinnam,et al.  Support vector machines for recognizing shifts in correlated and other manufacturing processes , 2002 .

[59]  Chun-Chin Hsu,et al.  Intelligent ICA-SVM fault detector for non-Gaussian multivariate process monitoring , 2010, Expert Syst. Appl..

[60]  Kevin J. Dooley,et al.  Identification of change structure in statistical process control , 1992 .

[61]  Ahmed Ghorbel,et al.  A survey of control-chart pattern-recognition literature (1991-2010) based on a new conceptual classification scheme , 2012, Comput. Ind. Eng..

[62]  Esteban Alfaro,et al.  A boosting approach for understanding out-of-control signals in multivariate control charts , 2009 .

[63]  W. Woodall,et al.  Multivariate CUSUM Quality- Control Procedures , 1985 .

[64]  Ruey-Shiang Guh,et al.  An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts , 2008, Comput. Ind. Eng..

[65]  Huu Du Nguyen,et al.  Monitoring coefficient of variation using one-sided run rules control charts in the presence of measurement errors , 2020, 2001.01821.

[66]  A. Vries.  ‘Nonstationarity in statistical process control – issues, cases, ideas’ by B. De Ketelaere, K. Mertens, F. Mathijs, D. Sabin Diaz and J. De Baerdemaeker , 2011 .

[67]  Min Zhang,et al.  Enhancing the monitoring of 3D scanned manufactured parts through projections and spatiotemporal control charts , 2014, Journal of Intelligent Manufacturing.

[68]  R. Sparks,et al.  Detecting changes in location using distribution‐free control charts with big data , 2017, Qual. Reliab. Eng. Int..

[69]  M. Shewhart,et al.  Interpreting statistical process control (SPC) charts using machine learning and expert system techniques , 1992, Proceedings of the IEEE 1992 National Aerospace and Electronics Conference@m_NAECON 1992.

[70]  Andrew Y. T. Leung,et al.  Cointegration Testing Method for Monitoring Nonstationary Processes , 2009 .

[71]  William H. Woodall,et al.  The effect of autocorrelation on the retrospective X-chart , 1992 .

[72]  M. Ahsan,et al.  Tr(R2) control charts based on kernel density estimation for monitoring multivariate variability process , 2019, Cogent Engineering.

[73]  Chuen-Sheng Cheng,et al.  A NEURAL NETWORK APPROACH FOR THE ANALYSIS OF CONTROL CHART PATTERNS , 1997 .

[74]  Jens Sadowski,et al.  Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification , 2003, J. Chem. Inf. Comput. Sci..

[75]  Yi-Chih Hsieh,et al.  A neural network based model for abnormal pattern recognition of control charts , 1999 .

[76]  Massimo Pacella,et al.  A comparison study of control charts for statistical monitoring of functional data , 2010 .

[77]  Ming J. Zuo,et al.  A threshold self-setting condition monitoring scheme for wind turbine generator bearings based on deep convolutional generative adversarial networks , 2021 .

[78]  Chih-Ming Hsu,et al.  Analysis of variations in a multi-variate process using neural networks , 2003 .

[79]  Rassoul Noorossana,et al.  Effect of Autocorrelation on Performance of the MCUSUM Control Chart , 2006, Qual. Reliab. Eng. Int..

[80]  Wei Jiang,et al.  High-Dimensional Process Monitoring and Fault Isolation via Variable Selection , 2009 .

[81]  Fugee Tsung,et al.  A kernel-distance-based multivariate control chart using support vector methods , 2003 .

[82]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[83]  Peihua Qiu,et al.  Transparent Sequential Learning for Statistical Process Control of Serially Correlated Data , 2021, Technometrics.

[84]  Hui Yang,et al.  Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control , 2017, Commun. Stat. Simul. Comput..

[85]  Xiaojun Zhou,et al.  Identifying source(s) of out-of-control signals in multivariate manufacturing processes using selective neural network ensemble , 2009, Eng. Appl. Artif. Intell..

[86]  Bedir Tekinerdogan,et al.  Machine learning applications in production lines: A systematic literature review , 2020, Comput. Ind. Eng..

[87]  Chun-Chin Hsu,et al.  Integrating independent component analysis and support vector machine for multivariate process monitoring , 2010, Comput. Ind. Eng..

[88]  Wei Zhou,et al.  Autoregressive coefficient-invariant control chart pattern recognition in autocorrelated manufacturing processes using neural network ensemble , 2013, Journal of Intelligent Manufacturing.

[89]  D. D. Diren,et al.  Integration of Machine Learning Techniques and Control Charts for Multivariate Processes , 2019, Scientia Iranica.

[90]  Giovanna Capizzi,et al.  A Least Angle Regression Control Chart for Multidimensional Data , 2011, Technometrics.

[91]  Yuehjen E. Shao,et al.  Mixture control chart patterns recognition using independent component analysis and support vector machine , 2011, Neurocomputing.

[92]  Jia Wu,et al.  Efficient hyperparameter optimization through model-based reinforcement learning , 2020, Neurocomputing.

[93]  Sébastien Thomassey,et al.  An anomaly detection approach based on the combination of LSTM autoencoder and isolation forest for multivariate time series data , 2020 .

[94]  Lei Lin,et al.  Self-Supervised Pre-Training of Transformers for Satellite Image Time Series Classification , 2020 .

[95]  Fugee Tsung,et al.  Improved design of kernel distance–based charts using support vector methods , 2013 .

[96]  Jyrki Kullaa,et al.  DAMAGE DETECTION OF THE Z24 BRIDGE USING CONTROL CHARTS , 2003 .

[97]  W. Schmid,et al.  Surveillance of non-stationary processes , 2018, AStA Advances in Statistical Analysis.

[98]  Mojtaba Salehi,et al.  On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model , 2011, Neurocomputing.

[99]  Mykola Pechenizkiy,et al.  An Overview of Concept Drift Applications , 2016 .

[100]  Javier M. Moguerza,et al.  A review of machine learning kernel methods in statistical process monitoring , 2020, Comput. Ind. Eng..

[101]  Stephen E. Fienberg,et al.  Current and Potential Statistical Methods for Monitoring Multiple Data Streams for Biosurveillance , 2006 .

[102]  Jing Li,et al.  Fault detection and isolation of faults in a multivariate process with Bayesian network , 2010 .

[103]  Jianbo Yu,et al.  Stacked denoising autoencoder‐based feature learning for out‐of‐control source recognition in multivariate manufacturing process , 2018, Qual. Reliab. Eng. Int..

[104]  Xiaoh Wang.  Hybrid Abnormal Patterns Recognition of Control Chart Using Support Vector Machining , 2008, 2008 International Conference on Computational Intelligence and Security.

[105]  Jamal Arkat,et al.  Artificial neural networks in applying MCUSUM residuals charts for AR(1) processes , 2007, Appl. Math. Comput..

[106]  G. A. Pugh.  Synthetic neural networks for process control , 1989 .

[107]  Philippe Castagliola,et al.  Effect of measurement error and autocorrelation on the X¯ chart , 2011 .

[108]  Seoung Bum Kim,et al.  Principal component analysis-based control charts for multivariate nonnormal distributions , 2013, Expert Syst. Appl..

[109]  Seoung Bum Kim,et al.  Process monitoring using variational autoencoder for high-dimensional nonlinear processes , 2019, Eng. Appl. Artif. Intell..

[110]  Krystel K. Castillo-Villar,et al.  An Improved multivariate generalised likelihood ratio control chart for the monitoring of point clouds from 3D laser scanners , 2018, Int. J. Prod. Res..

[111]  Huu Du Nguyen,et al.  Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management , 2020, Int. J. Inf. Manag..

[112]  Guan Yu,et al.  Outlier Detection in Functional Observations With Applications to Profile Monitoring , 2012, Technometrics.

[113]  Kim Phuc Tran,et al.  Anomaly detection using Long Short Term Memory Networks and its applications in Supply Chain Management , 2019, IFAC-PapersOnLine.

[114]  Duc Truong Pham,et al.  Feature-based control chart pattern recognition , 1997 .

[115]  Truong Thu Huong,et al.  Data driven hyperparameter optimization of one-class support vector machines for anomaly detection in wireless sensor networks , 2017, ATC 2017.

[116]  Shumei Chen,et al.  Deep recurrent neural network‐based residual control chart for autocorrelated processes , 2019, Qual. Reliab. Eng. Int..

[117]  Seoung Bum Kim,et al.  One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes , 2010, Commun. Stat. Simul. Comput..

[118]  Peihua Qiu,et al.  Big Data? Statistical Process Control Can Help! , 2020 .

[119]  Seyed Taghi Akhavan Niaki,et al.  Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks , 2005 .

[120]  W. Schmid,et al.  Challenges in Monitoring Non-stationary Time Series , 2018 .

[121]  Sheng Hu,et al.  A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines , 2013 .

[122]  Layth C. Alwan.  Effects of autocorrelation on control chart performance , 1992 .

[123]  James Stephen Marron,et al.  Kernel Quantile Estimators , 1990 .

[124]  Peihua Qiu,et al.  Statistical Process Control Charts as a Tool for Analyzing Big Data , 2017 .

[125]  Farid Kadri,et al.  Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems , 2016, Neurocomputing.

[126]  Jun-Geol Baek,et al.  Real-time contrasts control chart using random forests with weighted voting , 2017, Expert Syst. Appl..

[127]  Min Zhang,et al.  An EWMA and region growing based control chart for monitoring image data , 2019, Quality Technology & Quantitative Management.

[128]  Mohamed Limam,et al.  Support vector regression based residual MCUSUM control chart for autocorrelated process , 2008, Appl. Math. Comput..

[129]  Guan Wang,et al.  Diagnostic monitoring of high-dimensional networked systems via a LASSO-BN formulation , 2017 .

[130]  Ratna Babu Chinnam,et al.  Using support vector machines for recognizing shifts in correlated manufacturing processes , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[131]  Zhihao Liu,et al.  Control chart pattern recognition using the convolutional neural network , 2019, Journal of Intelligent Manufacturing.

[132]  Edgard M. Maboudou-Tchao.  Change detection using least squares one-class classification control chart , 2020 .

[133]  Ruey-Shiang Guh,et al.  Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach , 2011, Comput. Ind. Eng..

[134]  D. T. Pham,et al.  Estimation and generation of training patterns for control chart pattern recognition , 2016, Comput. Ind. Eng..

[135]  Douglas C. Montgomery,et al.  Some Current Directions in the Theory and Application of Statistical Process Monitoring , 2014 .

[136]  Deovrat Kakde,et al.  A non-parametric control chart for high frequency multivariate data , 2016, 2017 Annual Reliability and Maintainability Symposium (RAMS).

[137]  Soodeh Hosseini,et al.  New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN , 2020, Comput. Networks.

[138]  F. Y. Edgeworth,et al.  XLI. On discordant observations , 1887 .

[139]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[140]  Javier Tarrío-Saavedra,et al.  Constructing a Control Chart Using Functional Data , 2020, Mathematics.

[141]  Gamini P. Mendis,et al.  Monitoring of a machining process using kernel principal component analysis and kernel density estimation , 2019, Journal of Intelligent Manufacturing.

[142]  Shijin Wang,et al.  A deep autoencoder feature learning method for process pattern recognition , 2019, Journal of Process Control.

[143]  Fadel M. Megahed,et al.  An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products , 2016 .

[144]  Zhen He,et al.  Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques , 2013, J. Intell. Manuf..

[145]  Fadel M. Megahed,et al.  Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective , 2016 .

[146]  Pingyu Jiang,et al.  Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function , 2018, J. Intell. Manuf..

[147]  Monitoring Image Processes: Overview and Comparison Study , 2021, Frontiers in Statistical Quality Control 13.

[148]  Wei Jiang,et al.  A distance-based control chart for monitoring multivariate processes using support vector machines , 2016, Annals of Operations Research.

[149]  Truong Thu Huong,et al.  Real Time Data-Driven Approaches for Credit Card Fraud Detection , 2018, ICEBA 2018.

[150]  Mohamed Limam,et al.  Performance Evaluation of One‐Class Classification‐based Control Charts through an Industrial Application , 2013, Qual. Reliab. Eng. Int..

[151]  C. Heuchenne,et al.  Monitoring the coefficient of variation using variable sampling interval CUSUM control charts , 2020, Journal of Statistical Computation and Simulation.

[152]  Yuan Yuan,et al.  Self-Supervised Pretraining of Transformers for Satellite Image Time Series Classification , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[153]  Miin-Shen Yang,et al.  A fuzzy-soft learning vector quantization , 2003, Neurocomputing.

[154]  Mohamed Limam,et al.  A One-Class Classification-Based Control Chart Using the -Means Data Description Algorithm , 2014 .

[155]  Ataollah Ebrahimzadeh,et al.  Control chart pattern recognition using a novel hybrid intelligent method , 2011, Appl. Soft Comput..