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A High-dimensional control Chart for Profile Monitoring

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2011年第4期27卷 451-464页

作者： Chen, Shuohui Nembhard, Harriet Black Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16801 USA

Profile monitoring is an important and rapidly emerging area of statistical process control. In many industries, the quality of processes or products can be characterized by a profile that describes a relationship or a function between a response variable and one or more independent variables. A change in the profile relationship can indicate a change in the quality characteristic of the process or product and, therefore, needs to be monitored for control purposes. We propose a high-dimensional (HD) control chart approach for profile monitoring that is based on the adaptive Neyman test statistic for the coefficients of discrete Fourier transform of profiles. We investigate both linear and nonlinear profiles, and we study the robustness of the HD control chart for monitoring profiles with stationary noise. We apply our control chart to monitor the process of nonlinear woodboard vertical density profile data of Walker and Wright (J. Qual. Technol. 2002;34: 118-129) and compare the results with those presented in Williams et al. (Qual. Reliab. Eng. Int. 2007;to appear). Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： adaptive Neyman test statistic autoregressive models discrete Fourier transform multivariate statistical process control profile data time series models

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A special variable sample size and sampling interval Hotelling's T ² chart

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2011年第1-4期53卷 379-384页

作者： Mahadik, Shashibhushan B. Shirke, Digambar T. DRK Coll Commerce Dept Stat Kolhapur 416002 Maharashtra India Shivaji Univ Dept Stat Kolhapur 416004 Maharashtra India

Among the variable sampling interval (VSI), variable sample size (VSS), and variable sample size and sampling interval (VSSI) T (2) charts that use two values of each adaptive parameter, a VSI T (2) chart is the most efficient chart to detect large shifts in the process mean vector while a VSSI T (2) chart is the most efficient one to detect small shifts. The statistical performance of the T (2) chart proposed in this paper very closely matches that of such VSI and VSSI T (2) charts for detecting the large and small shifts, respectively. The proposed chart uses two values of the sampling interval and three values of the sample size.

关键词： Adaptive control chart multivariate statistical process control Steady-state average time to signal Markov chain

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Integration of classification algorithms and control chart techniques for monitoring multivariate processes

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JOURNAL OF statistical COMPUTATION AND SIMULATION 2011年第12期81卷 1897-1911页

作者： Sukchotrat, Thuntee Kim, Seoung Bum Tsui, Kwok-Leung Chen, Victoria C. P. Korea Univ Sch Ind Management Engn Seoul South Korea Univ Texas Arlington Dept Ind & Mfg Syst Engn Arlington TX 76019 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

We propose new multivariate control charts that can effectively deal with massive amounts of complex data through their integration with classification algorithms. We call the proposed control chart the 'Probability of Class (PoC) chart' because the values of PoC, obtained from classification algorithms, are used as monitoring statistics. The control limits of PoC charts are established and adjusted by the bootstrap method. Experimental results with simulated and real data showed that PoC charts outperform Hotelling's T-2 control charts. Further, a simulation study revealed that a small proportion of out-of-control observations are sufficient for PoC charts to achieve the desired performance.

关键词： data mining Hotelling's T-2 multivariate statistical process control supervised classification method

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SPC for short-run multivariate autocorrelated processes

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JOURNAL OF APPLIED STATISTICS 2011年第10期38卷 2303-2312页

作者： Snoussi, A. Univ Tunis Higher Inst Management 41 Tunis 2000 Tunisia

This paper discusses the development of a multivariate control charting technique for short-run autocorrelated data manufacturing environment. The proposed approach is a combination of the multivariate residual charts for autocorrelated data and the multivariate transformation technique for i.i.d. process observations of short lengths. The proposed approach consists in fitting adequate multivariate time-series model of various process outputs and computes the residuals, transforming them into standard normal N(0, 1) data and then using standardized data as inputs to plot conventional univariate i.i.d. control charts. The objective for applying multivariate finite horizon techniques for autocorrelated processes is to allow continuous process monitoring, since all process outputs are controlled trough the use of a single control chart with constant control limits. Throughout simulated examples, it is shown that the proposed short-run process monitoring technique provides approximately similar shifts detection properties as VAR residual charts.

关键词： time-series model univariate statistical process control multivariate statistical process control SCC control charts VAR Residual control charts V statistics T-2 statistics average run length

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An Overview of Fault Detection Techniques in Automated Monitoring Systems

An Overview of Fault Detection Techniques in Automated Monit...

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11th Scandinavian Conference on Artificial Intelligence (SCAI)

作者： Zhong, Shengtong Norwegian Univ Sci & Technol Dept Comp & Informat Sci NO-7491 Trondheim Norway

ISBN: (纸本)9781607507543;9781607507536

This paper gives an overview of different methods for automated fault detection. Emphasis will be put on the properties of model based techniques (which we will further divide into analytical model based and knowledge based), multivariate statistical process control and machine learning techniques. The machine learning techniques are not traditionally viewed as a standard method for fault detection, so they are especially highlighted in this paper. Each method is presented in detail, and we will also discuss alternative extensions for various applications. The paper is ended by proposing to use machine learning techniques as a robust and well-functioning method in general.

关键词： Fault detection model based techniques multivariate statistical process control machine learning methods

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Heteroscedastic latent variable modelling with applications to multivariate statistical process control

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2006年第1期80卷 57-66页

作者： Reis, MS Saraiva, PM Univ Coimbra Dept Chem Engn GEPSI PSE Grp P-3030290 Coimbra Portugal

We present an approach for conducting multivariate statistical process control (MSPC) in noisy environments, i.e., when the signal to noise ratio is low, and, furthermore, noise standard deviation (uncertainty) affecting each collected value can vary over time, and is assumingly known. This approach is based upon a latent variable model structure, HLV (standing for heteroscedastic latent variable model), that explicitly integrates information regarding data uncertainty. Moderate amounts of missing data can also be handled in a coherent and fully integrated way through HLV. Several examples show the added value achieved under noisy conditions by adopting such an approach and a case study illustrates its application to a real industrial context of pulp and paper product quality data analysis. (c) 2005 Elsevier B.V. All rights reserved.

关键词： multivariate statistical process control measurement uncertainty latent variable modelling

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Support Vector Data Description based multivariate Cumulative Sum control Chart

Support Vector Data Description based Multivariate Cumulativ...

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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： He, Shuguang Zhang, Chunyan Tianjin Univ Sch Management Tianjin 300072 Peoples R China

ISBN: (纸本)9783037852156

A SVDD (Support Vector Data Description) based MCUSUM (multivariate Cumulative Sum) chart is proposed and referred as S-MCUSUM chart, which has an advantage of distribution free. Numerical experiments on the performance of the S-MCUSUM chart is compared to the COT (Cumulative of T) chart. The results show that the COT chart is somewhat better than the S-MCUSUM chart for multivariate normally distributed data. However, the S-MCUSUM chart is much better than the COT chart for banana-shaped distributed data which is a typical non-normal distribution.

关键词： Support vector data description Cumulative sum chart multivariate statistical process control One class classification

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Integrating multivariate engineering process control and multivariate statistical process control

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第1-2期29卷 129-136页

作者： Yang, Ling Shen, Shey-Huei Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan St Johns Univ Dept Ind Engn & Management Taipei Taiwan

multivariate engineering process control (MEPC) and multivariate statistical process control (MSPC) are two strategies for quality improvement that have developed independently. MEPC aims to minimize variability by adjusting process variables to keep the process output on target. On the other hand, MSPC aims to reduce variability by monitoring and eliminating assignable causes of variation. In this paper, the use of MEPC alone is compared to using the MEPC coupled with MSPC. We use simulations to evaluate the average run lengths (ARL) and the averages of the performance measure. The simulation results show that the use of both MEPC and MSPC can always outperform the use of either alone. To detect small sustained shifts of the mean vector, combing MEPC with a multivariate generally weighted moving average (MGWMA) chart (MEPC/MGWMA) is more sensitive than the MEPC/multivariate exponentially weighted moving average (MEWMA) chart and MEPC/Hotelling's chi(2) chart. An example of the application, based on the proposed method, is also given.

关键词： automatic process control control chart EWMA GWMA multivariate engineering process control multivariate statistical process control

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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 Batch processes with Multiple On-Off Steps in Semiconductor Manufacturing

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JOURNAL OF QUALITY TECHNOLOGY 2011年第2期43卷 142-157页

作者： Lee, Shui-Pin Chao, An-Kuo Tsung, Fugee Wong, David Shan Hill Tseng, Sheng-Tsiang Jang, Shi-Shang Ching Yun Univ Dept Ind Engn & Management Tao Yuan 32097 Taiwan Natl Tsing Hua Univ Inst Stat Hsinchu 30013 Taiwan Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Kowloon Hong Kong Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 30013 Taiwan

A modern semiconductor manufacturing line is made of hundreds of sequential batch-processing stages. Each of these stages consists of many steps carried out by expensive tools, which are monitored by numerous sensors capable of sampling at intervals of seconds. The sensor readings of each run constitute profiles, which can include extremely drastic changes. The heterogeneous variations at different profile points are mainly due to on-off recipe actions at specific points. In addition, the analysis of these profiles is further complicated by long-term trends due to tool aging and short-term effects specific to the first wafer in a lot cycle. statistical process control methods that fail to take these effects into consideration will lead to frequent false alarms. A systematic method is proposed to address these challenges. First, a reference profile is determined for each sensor variable that describes the on-off actions. Next, level shifts of these profiles in each step are established to capture and remove intrinsic variations due to long-term aging trends and the short-term first-wafer effects. The residuals are used to formulate a health index, and this index can be used to monitor the health of the equipment and detect faulty wafers efficiently.

关键词： Batch-processing Stages Health Index Heterogeneous Variation multivariate statistical process control Sensor Data Signal Map

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