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Nonparametric monitoring of multivariate data via KNN learning

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第20期59卷 6311-6326页

作者： Li, Wendong Zhang, Chi Tsung, Fugee Mei, Yajun Shanghai Univ Finance & Econ Sch Stat & Management Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Decis Analyt Kowloon Hong Kong Peoples R China Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they are sensitive to the parametric model assumptions of multivariate data. In this paper, we propose a novel, nonparametrick-nearest neighbours empirical cumulative sum (KNN-ECUSUM) control chart that is a machine-learning-based black-box control chart for monitoring multivariate data by utilising extensive historical data under both in-control and out-of-control scenarios. Our proposed method utilises thek-nearest neighbours (KNN) algorithm for dimension reduction to transform multivariate data into univariate data and then applies the CUSUM procedure to monitor the change on the empirical distribution of the transformed univariate data. Extensive simulation studies and a real industrial example based on a disk monitoring system demonstrate the robustness and effectiveness of our proposed method.

关键词： multivariate statistical process control KNN algorithm machine learning categorical variable CUSUM empirical probability mass function

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Ischemic Stroke Detection by Analyzing Heart Rate Variability in Rat Middle Cerebral Artery Occlusion Model

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2018年第6期26卷 1152-1160页

作者： Kodama, Tomonobu Kamata, Keisuke Fujiwara, Koichi Kano, Manabu Yamakawa, Toshitaka Yuki, Ichiro Murayama, Yuichi Jikei Univ Sch Med Dept Neurosurg Tokyo 1058471 Japan Kyoto Univ Dept Syst Sci Kyoto 6158085 Japan Kumamoto Univ Prior Org Innovat & Excellence Kumamoto 8608555 Japan

Although early reperfusion therapy is effective for acute ischemic stroke, limited therapeutic time-window resulted in only 10% of patients receiving reperfusion therapy. A fast and reliable stroke detection method is desired so that patients can receive early reperfusion therapy. It has been reported that ischemic stroke affects heart rate variability (HRV), which reflects activities of the autonomic nervous function. Thus, ischemic stroke may be detected at an acute stage through monitoring HRV. This paper proposes an HRV-based ischemic stroke detection algorithm by using multivariate statistical process control (MSPC), which is a well-known anomaly detection algorithm. As a feasibility study before collecting a large amount of clinical data from human patients, this paper used the middle cerebral artery occlusion (MCAO) model in rats for collecting HRV data shortly after ischemic stroke onsets. The 11 MCAO-operated rats and 11 sham-operated rats were prepared, and HRV data of three sham-operated rats were used for model construction. The data on the other 19 rats were used for its validation. The experimental result showed that sensitivity and specificity of the proposed algorithm were 82% and 75%, respectively. Thus, the present work shows the possibility of realizing an HRV-based ischemic stroke detection system for human patients.

关键词： Stroke detection middle cerebral artery occlusion model heart rate variability multivariate statistical process control anomaly detection

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Monitoring through T² Hotelling of cylinder lubrication process of marine diesel engine

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APPLIED THERMAL ENGINEERING 2017年 110卷 32-38页

作者： Boullosa, David Luis Larrabe, Juan Lopez, Alberto Angel Gomez, Miguel Univ Basque Country UPV EHU Dept Naut Sci & Marine Syst Engn Maria Diaz de Haro 68 Portugalete 48920 Spain

Frequently, it can be complex to identify when a process, has a deviation from its normal working conditions, when the process variables are individually monitored. This paper responds to that need. The cylinder lubrication process, of a 2T marine diesel engine, was monitored throughout multivariate control charts, for specific ship operation conditions;the historical data set in control the optimal working process was made and with a new entry of sample n = 23, observations out of statistical control were detected using Hotelling's T-2 control charts. The variables that generated the signals in the process were identified through the MYT decomposition technique. This technique could complement and not supplement the systems which monitor the independent variables and have a general vision of the behavior of the complete process. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multivariate statistical process control Hotelling's T-2 chart MYT decomposition Marine diesel engine Performance monitoring

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An adaptive step-down procedure for fault variable identification

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第11期54卷 3187-3200页

作者： Kim, Jinho Jeong, Myong K. Elsayed, Elsayed A. Al-Khalifa, K. N. Hamouda, A. M. S. Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ USA Qatar Univ Dept Mech & Ind Engn Doha Qatar

In a process with a large number of process variables (high-dimensional process), identifying which variables cause an out-of-control signal is a challenging issue for quality engineers. In this paper, we propose an adaptive step-down procedure using conditional T-2 statistic for fault variable identification. While existing procedures focus on selecting variables that have strong evidence of a change, the proposed step-down procedure selects a variable having the weakest evidence of a change at each step based on the variables that are selected in previous steps. The information of selected unchanged variables is effectively utilised in obtaining a powerful conditional T-2 test statistic for identifying the changed elements of the mean vector. The proposed procedure is designed to utilise the correlation information between fault and non-fault variables for the efficient fault variables identification. Further, the simulation results show that the proposed procedure has the better diagnostic performance compared with existing methods in terms of fault variable identification and computational complexity, especially when the number of the variables is high and the number of fault variables is small.

关键词： conditional T-2 statistic fault variable identification multivariate statistical process control regression adjusted variables

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A distribution-free control chart for monitoring high-dimensional processes based on interpoint distances

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NAVAL RESEARCH LOGISTICS 2018年第4期65卷 317-330页

作者： Shu, Lianjie Fan, Jinyu Univ Macau Fac Business Adm Taipa Macau Peoples R China

With rapid advances in sensing technology and data acquisition systems, high-dimensional data appear in many settings. The high dimensionality presents a new challenge to the traditional tools in multivariate statistical process control, due to the "curse of dimensionality." Various tests for mean vectors in high dimensional situations have been discussed recently;however, they have been rarely adapted to process monitoring. This paper develops a distribution-free control chart based on interpoint distances for monitoring mean vectors in high-dimensional settings. Other than the Euclidean distance, the family of Minkowski distance is used for discussion, which is a generalization of the former and other distances. The proposed approach is very general as it represents a class of distribution-free control charts based on distances. Numerical results show that the proposed control chart is efficient in detecting mean shifts in both symmetric and heavy-tailed distributions.

关键词： control chart Minkowski distance multivariate statistical process control

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Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2019年第6期66卷 1769-1778页

作者： Fujiwara, Koichi Abe, Erika Kamata, Keisuke Nakayama, Chikao Suzuki, Yoko Yamakawa, Toshitaka Hiraoka, Toshihiro Kano, Manabu Sumi, Yukiyoshi Masuda, Fumi Matsuo, Masahiro Kadotani, Hiroshi Kyoto Univ Dept Syst Sci Kyoto 6068501 Japan Tokyo Med & Dent Univ Tokyo Japan Kumamoto Univ Kumamoto Japan Shiga Univ Med Sci Otsu Shiga Japan

Objective: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. Methods: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. Result: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. Conclusion: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. Significance: The proposed method can contribute to preventing accidents caused by drowsy driving.

关键词： Drowsy driving detection heart rate variability analysis electroencephalography anomaly detection multivariate statistical process control

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A new multivariate CUSUM chart using principal components with a revision of Crosier's chart

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2018年第2期47卷 464-476页

作者： Chen, Jiaqi Yang, Hualong Yao, Jianfeng Harbin Inst Technol Dept Math Harbin Heilongjiang Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hongkong Peoples R China

In this article, we introduce a new multivariate cumulative sum chart, where the target mean shift is assumed to be a weighted sum of principal directions of the population covariance matrix. This chart provides an attractive performance in terms of average run length (ARL) for large-dimensional data and it also compares favorably to existing multivariate charts including Crosier's benchmark chart with updated values of the upper control limit and the associated ARL function. In addition, Monte Carlo simulations are conducted to assess the accuracy of the well-known Siegmund's approximation of the average ARL function when observations are normal distributed. As a byproduct of the article, we provide updated values of upper control limits and the associated ARL function for Crosier's multivariate CUSUM chart.

关键词： Average run length Crosier's multivariate CUSUM chart multivariate CUSUM multivariate statistical process control

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Variable Selection-based multivariate Cumulative Sum control Chart

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2017年第3期33卷 565-578页

作者： Abdella, Galal M. Al-Khalifa, Khalifa N. Kim, Sangahn Jeong, Myong K. Elsayed, Elsayed A. Hamouda, Abdel Magid Qatar Univ Dept Mech & Ind Engn Doha Qatar Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ USA

High-dimensional applications pose a significant challenge to the capability of conventional statistical process control techniques in detecting abnormal changes in process parameters. These techniques fail to recognize out-of-control signals and locate the root causes of faults especially when small shifts occur in high-dimensional variables under the sparsity assumption of process mean changes. In this paper, we propose a variable selection-based multivariate cumulative sum (VS-MCUSUM) chart for enhancing sensitivity to out-of-control conditions in high-dimensional processes. While other existing charts with variable selection techniques tend to show weak performances in detecting small shifts in process parameters due to the misidentification of the faulty' parameters, the proposed chart performs well for small process shifts in identifying the parameters. The performance of the VS-MCUSUM chart under different combinations of design parameters is compared with the conventional MCUSUM and the VS-multivariate exponentially weighted moving average control charts. Finally, a case study is presented as a real-life example to illustrate the operational procedures of the proposed chart. Both the simulation and numerical studies show the superior performance of the proposed chart in detecting mean shift in multivariate processes. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： average run length (ARL) multivariate statistical process control VS-MCUSUM VS-MEWMA variable selection

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statistical process control for monitoring non-linear profiles using wavelet filtering and B-Spline approximation

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2010年第4期48卷 1049-1068页

作者： Chang, Shing I. Yadama, Srikanth Kansas State Univ Dept Ind & Mfg Syst Engn Manhattan KS 66506 USA

A statistical process control framework is proposed to monitor non-linear profiles. The proposed methodology aims at identifying mean shifts or 'shape changes' in a profile. Discrete wavelet transformation (DWT) is applied to separate variation or noise from profile contours. B-splines are adopted to generate critical points to define the shape of a profile. The proposed method is innovative in that users can divide a profile into multiple segments to be monitored simultaneously. The high dimensionality problem that hinders the implementation of multivariate control charts can be solved by this framework. The distance difference statistic for each segment provides diagnostic information when the process of interest is out of control. These proposed statistics form a vector to be fed into any multivariate control chart such as the Hotelling T-2 control chart. A decomposition method can also be applied on the T-2 statistics when an out-of-control profile is detected. A simulation study applied to a forging process is conducted to demonstrate the property of the proposed method. The proposed method is capable of detecting profile shifts and identifying the exact location of problematic segments.

关键词： multivariate statistical process control wavelet transformation B-spline approximation Hotelling T-2 statistic

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On the internal multivariate quality control of analytical laboratories.: A case study:: the quality of drinking water

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2001年第2期56卷 93-103页

作者： Ortiz-Estarelles, O Martín-Biosca, Y Medina-Hernández, MJ Sagrado, S Bonet-Domingo, E Univ Valencia Fac Farm Dept Quim Analit E-46100 Burjassot Spain Gen Anal Mat & Serv GAMASER SL Valencia Spain

multivariate statistical process control (MSPC) tools, based on principal component analysis (PCA), partial least squares (PLS) regression and other regression models, are used in the present study for automatic detection of possible errors in the methods used for routine multiparametric analysis in order to design an internal multivariate Analytical Quality control (iMAQC) program, Such tools could notice possible failures in the analytical methods without resorting to any external reference since they use their own analytical results as a source for the diagnosis of the method's quality. Pseudo-univariate control charts provide an attractive alternative to traditional univariate and multivariate control charts. This approach uses the relative prediction error in percentage, Er (%), which is calculated from a multivariate model such as PLS, as the univariate control variable. Er offers quantitative information on the magnitude of the error and is sensitive to systematic errors at the 10% level (which are of analytical interest). Finally, its capacity to detect/quantify error in a single method can be checked a priori, As a case study for applying such strategies in routine analysis, the problem of the quality of drinking water was examined. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： quality control control charts multivariate statistical process control multivariate regression PCA PLS routine water analysis

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