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Monitoring multivariate process variability via eigenvalues

COMPUTERS & INDUSTRIAL ENGINEERING 2017年 113卷 269-281页

作者： Fan, Jinyu Shu, Lianjie Zhao, Honghao Yeung, Hangfai Univ Macau Fac Business Adm Zhuhai Guangdong Peoples R China Macau Univ Sci & Technol Sch Business Macau Peoples R China

Various methods have been proposed to monitor changes in a process covariance matrix. In view that a covariance matrix can be fully defined by its eigenvalues and eigenvectors, this paper suggests monitoring the covariance matrix based on eigenvalues as another alternative. Although there are some recent discussions about the use of eigenvalues for hypothesis testing in multivariate analysis, the use of them for monitoring covariance matrix changes has been less studied in multivariate quality control. The simulation results show that the proposed method performs especially well under simultaneous shifts in both variance and correlation elements and competitively under shifts in variance or correlation elements only, compared to the existing approaches. This demonstrates a good property of the proposed method being able to provide a robust detection performance under a wide variety of scenarios. A real example is also provided to illustrate the implementation of the proposed method.

关键词： multivariate statistical process control Variability Sparsity Covariance matrix Eigenvalues

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controlling Bivariate Categorical processes using Scan Rules

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2017年第4期19卷 1135-1149页

作者： Bersimis, Sotirios Sachlas, Athanasios Castagliola, Philippe Univ Piraeus Dept Stat & Insurance Sci 80 Karaoli & Dimitriou Str Piraeus 18534 Greece Univ Nantes LUNAM Univ 2 Ave Prof Jean RouxelBP 539 F-44475 Carquefou France CNRS UMR 6597 IRCCyN 2 Ave Prof Jean RouxelBP 539 F-44475 Carquefou France

In this paper, we introduce a methodology for efficiently monitoring a health process that classify the intervention outcome, in two dependent characteristics, as "absolutely successful", "with minor but acceptable complications" and "unsuccessful due to severe complications". The monitoring procedure is based on appropriate 2-dimensional scan rules. The run length distribution is acquired by studying the waiting time distribution for the first occurrence of a 2-dimensional scan in a bivariate sequence of trinomial trials. The waiting time distribution is derived through a Markov chain embedding technique. The proposed procedure is applied on two simulated cases while it is tested against a competing method showing an excellent performance.

关键词： Multi-attribute processes Markov chain embeddable random variables multivariate statistical process control Waiting time distributions

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ARL Numerics for MEWMA Charts

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JOURNAL OF QUALITY TECHNOLOGY 2017年第1期49卷 78-89页

作者： Knoth, Sven Helmut Schmidt Univ Univ Fed Armed Forces Hamburg Dept Math & Stat POB 700822 D-22008 Hamburg Germany

The FORTRAN code in Bodden and Rigdon (1999) for the in-control average run length (ARL) of multivariate exponentially weighted moving average charts (MEWMA) became quite popular and is widely used in statistical software systems such as MINITAB and STATISTICA. We find that the algorithms' accuracy is poor for low-dimensional processes. The Markov chain approximation described in Runger and Prabhu (1996) is not able to resolve the issue. The same holds for the calculation of the out-of-control ARL as proposed in Ridgon (1995b). We present two concepts that achieve higher accuracy for all dimensions. The competing numerical procedures are implemented in the R package spc.

关键词： Collocation Fredholm Integral Equation of the Second Kind Markov Chain Approximation multivariate statistical process control Nystrom Method Software implementation

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On reducing false alarms in multivariate statistical process control

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2010年第4A期88卷 430-436页

作者： Chen, Tao Nanyang Technol Univ Sch Chem & Biomed Engn Singapore 637459 Singapore

The primary objective of this note is to reduce the false alarms in multivariate statistical process control (MSPC). The issue of false alarms is inherent within MSPC as a result of the definition of control limits. It has been observed that under normal operating conditions, the occurrence of "out-of-control" data, i.e. false alarms, conforms to a Bernoulli distribution. Therefore, this issue can be formally addressed by developing a Binomial distribution for the number of "out-of-control" data points within a given time window, and a second-level control limit can be established to reduce the false alarms. This statistical approach is further extended to consider the combination of multiple control charts. The proposed methodology is demonstrated through its application to the monitoring of a benchmark simulated chemical process, and it is observed to effectively reduce the false alarms whilst retaining the capability of detecting process faults. (C) 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Bernoulli distribution Binomial distribution False alarm multivariate statistical process control Principal component analysis

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An effective multivariate control chart for detecting small mean shifts using support vector data description

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ADVANCES IN MECHANICAL ENGINEERING 2018年第11期10卷

作者： Xia, Beixin Jian, Zheng Liu, Lei Li, Long Shanghai Univ Sch Management Shanghai Peoples R China Shanghai Univ Sch Mechatron Engn & Automat 149 Yanchang Rd Shanghai 200072 Peoples R China Apple Inc Cupertino CA USA

Conventional multivariate cumulative sum control charts are more sensitive to small shifts than T2 control charts, but they cannot get the knowledge of manufacturing process through the learning of in-control data due to the characteristics of their own structures. To address this issue, a modified multivariate cumulative sum control chart based on support vector data description for multivariate statistical process control is proposed in this article, which is named D-MCUSUM control chart. The proposed control chart will have both advantages of the multivariate cumulative sum control charts and the support vector data description algorithm, namely, high sensitivities to small shifts and learning abilities. The recommended values of some key parameters are also given for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the D-MCUSUM control chart. A real industrial case illustrates the application of the proposed control chart. The results also show that the D-MCUSUM control chart is more sensitive to small shifts than other traditional control charts (e.g. T2 and multivariate cumulative sum) and a D control chart based on support vector data description.

关键词： multivariate statistical process control multivariate cumulative sum chart support vector data description average run length quality control

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An extension to RPCA parameter selection and process monitoring

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IFAC-PapersOnLine 2017年第1期50卷 14764-14769页

作者： Rehmer, Alexander Kroll, Andreas Department of Measurement and Control Institute for System Analytics and Control Faculty of Mechanical Engineering University of Kassel Kassel Germany

multivariate statistical process control (MSPC) techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS) have found wide application especially in the statistical modeling and monitoring of chemical processes. However, real industrial processes often violate the assumptions underlying MSPC since they exhibit time-varying and non-stationary behavior. Adaptive PCA-based monitoring procedures such as Moving Window PCA (MWPCA) and Recursive PCA (RPCA) have been proposed to tackle this issue. Although the parameter selection for those procedures is critical to their proper implementation, this topic is rarely covered in the literature. This paper examines two methods for MWPCA and RPCA parameter selection using the Tennessee Eastman process as an example. Based on the findings a novel procedure for RPCA parameter selection as well as a extension to RPCA will be proposed and demonstrated. © 2017

关键词： Principal component analysis Chemical analysis Fault detection Least squares approximations Multivariant analysis process monitoring statistical process control Adaptive process monitoring Industrial processs multivariate statistical process control Non stationary behaviors Parameter selection Partial least square (PLS) statistical modeling Tennessee Eastman process

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Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis

Fault detection in the Tennessee Eastman benchmark process w...

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20th World Congress of the International-Federation-of-Automatic-control (IFAC)

作者： Krishnannair, S. Aldrich, C. Univ Zululand ZA-3886 Kwa Dlangezwa South Africa Univ Stellenbosch Dept Proc Engn Private Bag XI ZA-7602 Matieland South Africa Curtin Univ Western Australia Sch Mines Dept Min Engn & Met Engn GPO Box U1987 Perth WA 6845 Australia

multivariate statistical process monitoring methods aim at detecting and identifying faults in the performance of processes over time in order to keep the process under control. Singular spectrum analysis (SSA) is a potential tool for multivariate process monitoring. It allows the decomposition of dynamic process variables or time series into additive components that can be monitored separately to identify hidden faults that may otherwise not be detectable. However, SSA is a linear method and can give misleading information when it is applied to dynamic processes with strong nonlinearity. Therefore, in this paper, nonlinear versions of SSA based on the use of auto-associative neural networks or auto encoders and dissimilarity matrices are considered. This is done based on the benchmark Tennessee Eastman process that is widely used in the evaluation of statistical process monitoring methods. (C) 2017, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Fault detection and diagnosis multivariate statistical process control Singular Value Decomposition Singular Spectrum Analysis

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Structural Damage Fault Detection Using Artificial Neural Network Profile Monitoring 7

Structural Damage Fault Detection Using Artificial Neural Ne...

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7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)

作者： Awad, Mahmoud AlHamaydeh, Mohammad Mohamed, Ahmed Fares Amer Univ Sharjah Dept Ind Engn Sharjah U Arab Emirates Amer Univ Sharjah Dept Civil Engn Sharjah U Arab Emirates

ISBN: (纸本)9781509054541

In today's world, structural development with reliability and integrity is an ever demanding process. Fault detection is the identification of normal healthy behavior of a system or process and recognition of any deviation from such normal behavior. Fault detection in structural systems provides important liability and financial advantages since it gives the decision-makers lead-time and flexibility to manage the health of the system. Structural systems are critical systems that require continuous monitoring of damage accumulation caused by earthquake loads that may cause catastrophic failures. We present in this research a data-driven methodology for fault detection of structural systems using multivariate statistical process control (MVSPC). The proposed method based on modeling overall structural damage using artificial neural networks (ANN) as a function of the earthquake load intensity. Hotelling T-2 U technique is then used to identify any shifts of the ANN model weights from their healthy states. The proposed method is tested and validated using simulation data fora four-story RC building with varying concrete strengths. The methodology presented in this paper is scalable and can be applied to a wide range of systems to assess their health via an inspection check to anticipate and potentially avoid failures.

关键词： Structural damage profile monitoring multivariate statistical process control

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Fault detection in the Tennessee Eastman benchmark process with nonlinear singular spectrum analysis

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IFAC-PapersOnLine 2017年第1期50卷 8005-8010页

作者： Krishnannair S. Aldrich C. University of Zululand Kwadlangezwa3886 South Africa Department of Process Engineering University of Stellenbosch Private Bag XI Matieland 7602 South Africa Department of Mining Engineering and Metallurgical Engineering Western Australian School of Mines Curtin University GPO Box U1987WA 6845 Australia

multivariate statistical process monitoring methods aim at detecting and identifying faults in the performance of processes over time in order to keep the process under control. Singular spectrum analysis (SSA) is a potential tool for multivariate process monitoring. It allows the decomposition of dynamic process variables or time series into additive components that can be monitored separately to identify hidden faults that may otherwise not be detectable. However, SSA is a linear method and can give misleading information when it is applied to dynamic processes with strong nonlinearity. Therefore, in this paper, nonlinear versions of SSA based on the use of auto-associative neural networks or auto-encoders and dissimilarity matrices are considered. This is done based on the benchmark Tennessee Eastman process that is widely used in the evaluation of statistical process monitoring methods. © 2017

关键词： diagnosis Fault detection multivariate statistical process control Singular Spectrum Analysis Singular Value Decomposition

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Variable Selection for multivariate statistical process control

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JOURNAL OF QUALITY TECHNOLOGY 2010年第3期42卷 242-259页

作者： Gonzalez, Isabel Sanchez, Ismael Univ Carlos III Madrid Dept Mech Engn Madrid 28911 Spain Univ Carlos III Madrid Dept Stat Madrid 28911 Spain

This article proposes a methodology to select a subset of variables to measure and monitor for multivariate statistical process control (SPC). In contrast with most dimensionality reduction approaches for SPC, we reduce the number of variables that must be measured, thereby reducing the time and cost associated with inspection. We develop a two-stage procedure that selects the variables in a manner that retains as much information on the full set of variables as possible. In the first stage, the variables are sorted according to some measure of information content, which has broad applicability outside of SPC. In the second stage, the selected variables are determined using two alternative tools. The first tool is a general criterion based on the amount of residual information in the nonselected variables. The second tool is based on the performance of the control chart in detecting simulated out-of-control events. We illustrate the usefulness of the approach with simulation results and a real metal-forming application.

关键词： multivariate statistical process control Principal Variables Singular Value Decomposition Variable Selection

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