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A Distribution-free multivariate Change-point Model for statistical process control

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2015年第8期44卷 1975-1987页

作者： Zhou, Maoyuan Zi, Xuemin Geng, Wei Li, Zhonghua Civil Aviat Univ China Coll Sci Tianjin Peoples R China Nankai Univ LPMC Tianjin 300071 Peoples R China Nankai Univ Inst Stat Tianjin 300071 Peoples R China Tianjin Univ Technol & Educ Sch Sci Tianjin Peoples R China

This article develops a new distribution-free multivariate procedure for statistical process control based on minimal spanning tree (MST), which integrates a multivariate two-sample goodness-of-fit (GOF) test based on MST and change-point model. Simulation results show that our proposed procedure is quite robust to nonnormally distributed data, and moreover, it is efficient in detecting process shifts, especially moderate to large shifts, which is one of the main drawbacks of most distribution-free procedures in the literature. The proposed procedure is particularly useful in start-up situations. Comparison results and a real data example show that our proposed procedure has great potential for application.

关键词： Change-point Minimal spanning tree multivariate goodness-of-fit test multivariate statistical process control 62P30

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multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

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STATISTICS & PROBABILITY LETTERS 2015年 104卷 7-13页

作者： Nishimura, Kazuya Matsuura, Shun Suzuki, Hideo Meiji Univ Nakano Campus Div Tokyo 101 Japan Keio Univ Fac Sci & Technol Tokyo 108 Japan

In multivariate statistical process control, when a process shift occurs, not all variables but a few variables may shift from the in-control state. This paper proposes a multivariate EWMA control chart based on a var... 详细信息

关键词： Akaike information criterion Exponentially weighted moving average multivariate control chart multivariate statistical process control Variable selection

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Integrated use of ultrasound imaging and multivariate image analysis for detecting bone fragments in poultry meat

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FOOD RESEARCH INTERNATIONAL 2025年 206卷 116047页

作者： Collazos-Escobar, Gentil A. Lincetti, Elisa Spilimbergo, Sara Prats-Montalban, Jose M. Garcia-Perez, Jose, V Benedito, Jose Univ Politecn Valencia Inst Univ Ingn Alimentos FoodUPV Grp Anal & Simulac Proc Agroalimentarios ASPA Cami Vera S-NEdificio 3F Valencia 46022 Spain Univ Padua Dept Ind Engn I-35131 Padua Italy Univ Politecn Valencia UPV Grp Ingn Estadist Multivariante GIEM Camino Vera s-nEdificio7A Valencia 46022 Spain

Poultry meat industry requires intelligent systems for achieving non-invasive real-time detection of bone fragments. Therefore, the main aim of this study was to assess the feasibility of using ultrasound imaging and multivariate image analysis to detect bone fragments in boneless and skinless chicken breast fillets. Bone fragments of different sizes were inserted into the chicken and contact ultrasound images were acquired, following a pre-established pattern, in the control (C) and out-control (OC, with bone) samples, by scanning the breast's surface, using contact ultrasound sensors (1 MHz) working in through transmission. Energy-magnitude and energy-distribution ultrasound parameters were computed at pixel level in time (TDA) and frequency domain (FDA). Principal Component Analysis (PCA) was used in TDA and FDA parameters, and its combination (TFDA). From PCA model, the Residual Sum Squares (RSS) and Hotelling's T-square (T-2) control statistics were used to classify the C and OC images projected on the PCA latent structure. Experimental results demonstrated that the presence of bone fragments within chicken breast fillets led to alterations in the energy-magnitude (avg. amplitude decrease from 81.6 % to 52.6 %, depending on the bone size) and energy-distribution ultrasound parameters (avg. variance decreased from 97.9 % to 70.6 % depending on the bone size). The RSS statistic achieved the best classification performance (accuracy of TDA, FDA and TFDA>95 %) in C and OC images. These results highlight the potential of combining contact ultrasound imaging with multivariate image analysis for the reliable and rapid detection of bone fragments in chicken breasts.

关键词： Foreign bodies Chicken In-line quality monitoring Ultrasound imaging multivariate image analysis multivariate statistical process control

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statistical batch-based bearing fault detection

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JOURNAL OF MATHEMATICS IN INDUSTRY 2025年第1期15卷 1-20页

作者： Jorry, Victoria Duma, Zina-Sabrina Sihvonen, Tuomas Reinikainen, Satu-Pia Roininen, Lassi LUT Univ Yliopistonkatu 34 FI-53850 Lappeenranta Finland

In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning methods in diagnosing these faults. Based on the complex working conditions of rotary machines, multivariate statistical process control charts such as Hotelling's T2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$T<^>multivariate$\end{document} and Squared Prediction Error are useful for providing early warnings. However, these methods are rarely applied to condition monitoring of rotating machinery due to the univariate nature of the datasets. In the present paper, we propose a multivariate statistical process control-based fault detection method that utilizes multivariate data composed of Fourier transform features that are extracted for fixed-time batches. Our approach makes use of the multidimensional nature of Fourier transform characteristics, which record more detailed information about the machine's status, in an effort to enhance early defect detection and diagnosis. Experiments with varying vibration measurement locations (Fan End, Drive End), fault types (ball, inner, and outer race faults), and motor loads (0-3 horsepower) are used to validate the suggested approach. The outcomes illustrate our method's effectiveness in fault detection and point to possible wider uses in industrial maintenance.

关键词： Principal component analysis Fault detection multivariate statistical process control Fourier transformation Rolling-element bearing Vibration signal

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On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2012年第22期50卷 6288-6310页

作者： Du, Shichang Lv, Jun Xi, Lifeng Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Logist Engn Shanghai 200240 Peoples R China E China Normal Univ Sch Business Shanghai 200241 Peoples R China State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

In multivariate statistical process control (MSPC), most multivariate control charts can effectively monitor anomalies based on overall statistic, however, they cannot provide guidelines to classify the source(s) of out-of-control signals. Classifying the source(s) of process mean shifts is critical for quality control in multivariate manufacturing process since the immediate identification of them can greatly help quality engineer to narrow down the set of possible root causes and take corrective actions. This study presents an improved particle swarm optimisation with simulated annealing-based selective multiclass support vector machines ensemble (PS-SVME) approach, in which some selective multiclass SVMs are jointly used for classifying the source(s) of process mean shifts in multivariate control charts. The performance of the proposed PS-SVME approach is evaluated by computing its classification accuracy. Simulation experiments are conducted and a real application is illustrated to validate the effectiveness of the developed approach. The analysis results indicate that the developed PS-SVME approach can perform effectively for classifying the source(s) of process mean shifts.

关键词： multivariate statistical process control support vector machine ensemble classification process mean shifts

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A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第2期37卷 1035-1042页

作者： El-Midany, T. T. El-Baz, M. A. Abd-Elwahed, M. S. Mansoura Univ Fac Engn Prod Engn & Mech Design Dept Mansoura Egypt Zagazig Univ Fac Engn Dept Ind Engn Zagazig Egypt

This paper describes a proposed framework for multivariate process control chart recognition. The proposed methodology uses the Artificial Neural Networks (ANNs) to recognize set of subclasses of multivariate abnormal patterns. identify the responsible variable(s) oil the occurrence of abnormal pattern and classify the abnormal pattern parameters. The performance of the proposed approach has been evaluated using a real case study. The numerical and graphical results are presented which demonstrate that the approach performs effectively in control chart multivariate pattern recognition. In addition. accurately identifies and classifies the parameters of the errant variable(s). (C) 2009 Published by Elsevier Ltd

关键词： multivariate statistical process control multivariate control charts Pattern recognition Artificial neural networks

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Monitoring and Interpretation of process Variability Generated from the Integration of the multivariate Cumulative Sum control Chart and Artificial Intelligence

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APPLIED SCIENCES-BASEL 2024年第21期14卷 9705页

作者： Ruelas-Santoyo, Edgar Augusto Figueroa-Fernandez, Vicente Tapia-Esquivias, Moises Pantoja-Pacheco, Yaquelin Verenice Bravo-Santibanez, Edgar Cruz-Salgado, Javier Tecnol Nacl Mexico Celaya Dept Ind Engn Antonio Garcia Cubas 600 Celaya 38010 Guanajuato Mexico Ctr Med Nacl Bajio Intens Care Unit Blvd Adolfo Lopez Mateos 811 Leon De Los Aldama 37000 Guanajuato Mexico Univ Americas Puebla Dept Ind & Mech Engn Ex Hacienda Sta Catarina Martir S-N Cholula 72810 Puebla Mexico

Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems;otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems.

关键词： artificial neural network multivariate statistical process control

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multivariate process and quality monitoring applied to an electrolysis process Part I. process supervision with multivariate control charts

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 1998年第1-2期42卷 221-231页

作者： Wikstrom, C Albano, C Eriksson, L Friden, H Johansson, E Nordahl, A Rannar, S Sandberg, M Kettaneh-Wold, N Wold, S Umetri S-90719 Umea Sweden MDS Kinnelon NJ 07405 USA Umea Univ Inst Chem S-90187 Umea Sweden

multivariate statistical process control (MSPC) is applied to an electrolysis process. The process produces extremely pure copper, and to monitor its quality the levels of eight metal impurities were recorded twice a day. These quality data are analysed adopting an (1) 'intuitive' univariate approach, and (2) with multivariate techniques. It is demonstrated that the univariate analysis gives confusing results with regards to outlier detection, while the multivariate approach identifies two types of outliers. Moreover, it is shown how the results from the multivariate principal component analysis (PCA) method can be displayed graphically in multivariate control charts, multivariate Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are used and compared. Also, an informationally powerful control chart, the simultaneous scores monitoring and residual tracking (SMART) chart, is introduced and used. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： multivariate statistical process control PLS PCA EWMA control charts

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The process analytical technology initiative and multivariate process analysis, monitoring and control

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2006年第5期384卷 1043-1048页

作者： Kourti, T McMaster Univ Dept Chem Engn Hamilton ON L8S 4L7 Canada

process analytical technology is an essential step forward in pharmaceutical industry. Real-time analyzers will provide timely data on quality properties. This information combined with process data (temperatures, flow rates, pressure readings) collected in real time can become a powerful tool for this industry, for process understanding, process and quality monitoring, abnormal situation detection and for improving product quality and process reliability. A very important tool for this achievement is the multivariate analysis.

关键词： process analytical technology abnormal situation detection process analysis process understanding multivariate analysis multivariate statistical process control

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In-line process and product control using spectroscopy and multivariate calibration

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JOURNAL OF QUALITY TECHNOLOGY 2005年第1期37卷 1-20页

作者： Sahni, NS Aastveit, AH Naes, T Mills DA Res & Dev N-0506 Oslo Norway Agr Univ Norway Dept Chem Biotechnol & Food Sci N-1432 As Norway Norwegian Food Res Inst MATFORSK N-1400 As Norway

The quality of a product is dynamic in nature and develops over time. We present a case study from the food industry in which the concept of measuring the end-product quality is extended to incorporate the shelf-life period of the product with practical examples showing the harm of ignoring the changes that occur in the product quality as a function of time. The article also addresses the use of in-line spectroscopy to relate the variations in the input parameters, such as the raw materials, and the process variables to the final product quality over the entire shelf-life of the product. We also discuss multivariate statistical process control and monitoring issues.

关键词： in-line Spectroscopy longitudinal response data mixture-process variable design multivariate statistical process control split-plot designs

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