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22nd ACM Symposium on Applied Computing

作者： Chang, Ching-Pao Chu, Chih-Ping Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 701 Taiwan

statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

关键词： multivariate statistical process control causal analysis software process improvement

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Biological nitrogen removal process monitoring based on fuzzy robust PCA

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Research Journal of Applied Sciences, Engineering and Technology 2014年第21期7卷 4434-4444页

作者： Heloulou, Nabila Ramdani, Messaoud Abidi, Abdenabi Department of Electronics Laboratoire d'Automatique et Signaux de Annaba (LASA) Université Badji-Mokhtar de Annaba Annaba 23000 BP. 12 Algeria Department of Engineering Laboratoire d'Automatique et Signaux de Annaba (LASA) Université Badji-Mokhtar de Annaba Annaba 23000 BP. 12 Algeria

In this study the Fuzzy Robust Principal Component Analysis (FRPCA) method is used to monitor a biological nitrogen removal process, performances of this method are then compared with classical principal component analysis. The obtained results demonstrate the performances superiority of this robust extension compared with the conventional one. In this method fuzzy variant of PCA uses fuzzy membership and diminish the effect of outliers by assigning small membership values to outliers in order to make it robust. For the purpose of fault detection, the SPE index is used. Then the fault localization by contribution plots approach and SVI index are exploited. © Maxwell Scientific Organization, 2014.

关键词： Biological nitrogen removal Fault diagnosis Fuzzy robust PCA multivariate statistical process control process monitoring Water treatment plant

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Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2013年 125卷 101-108页

作者： Rato, Tiago J. Reis, Marco S. Univ Coimbra CIEPQPF Dept Chem Engn P-3030790 Coimbra Portugal

Current multivariate control charts for monitoring large scale industrial processes are typically based on latent variable models, such as principal component analysis (PCA) or its dynamic counterpart when variables present auto-correlation (DPCA). In fact, it is usually considered that, under such conditions, DPCA is capable to effectively deal with both the cross- and auto-correlated nature of data. However, it can easily be verified that the resulting monitoring statistics (T-2 and Q, also referred by SPE) still present significant auto-correlation. To handle this issue, a set of multivariate statistics based on DPCA and on the generation of decorrelated residuals were developed, that present low auto-correlation levels, and therefore are better positioned to implement SPC in a more consistent and stable way (DPCA-DR). The monitoring performance of these statistics was compared with that from other alternative methodologies for the well-known Tennessee Eastman process benchmark. From this study, we conclude that the proposed statistics had the highest detection rates on 19 out of the 21 faults, and are statistically superior to their PCA and DPCA counterparts. DPCA-DR statistics also presented lower auto-correlation, which simplifies their implementation and improves their reliability. (C) 2013 Elsevier B.V. All rights reserved.

关键词： multivariate statistical process control Principal component analysis Dynamic principal component analysis Missing data imputation Tennessee Eastman benchmark process

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Improved design of kernel distance-based charts using support vector methods

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IIE TRANSACTIONS 2013年第4期45卷 464-476页

作者： Ning, Xianghui Tsung, Fugee Beijing Jiaotong Univ Sch Mech Elect & Control Engn Dept Mech Engn Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Qual Lab Hong Kong Hong Kong Peoples R China

statistical process control (SPC) techniques that originated in manufacturing have also been used to monitoring the quality of various service processes, which can be characterized by one or several variables. In the literature, these variables are usually assumed to be either continuous or categorical. However, in reality, the quality characteristics of a service process may include both continuous and categorical variables (i.e., mixed-type variables). Direct application of conventional SPC techniques to monitor such mixed-type variables may cause increased false alarm rates and misleading conclusions. One promising solution is the kernel distancebased chart (K-chart), which makes use of Support Vector Machine (SVM) methods and requires no assumption on the variable distribution. This article provides an improved design of the SVM-based K-chart. A systematic approach to parameter selection for the considered charts is provided. An illustration and comparison are presented based on a real example from a logistics firm. The results confirm the improved performance obtained by using the proposed design scheme.

关键词： multivariate statistical process control K-chart kernel

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Chromatographic Peak Alignment Using Derivative Dynamic Time Warping

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BIOTECHNOLOGY PROGRESS 2013年第2期29卷 394-402页

作者： Bork, Christopher Ng, Kenneth Liu, Yinhan Yee, Alex Pohlscheidt, Michael Genentech Inc Mfg Sci & Technol Oceanside CA 92056 USA Univ Minnesota Dept Chem Engn Minneapolis MN 55455 USA Stanford Univ Dept Bioengn Stanford CA 94304 USA

Chromatogram overlays are frequently used to monitor inter-batch performance of bioprocess purification steps. However, the objective analysis of chromatograms is difficult due to peak shifts caused by variable phase durations or unexpected process holds. Furthermore, synchronization of batch process data may also be required prior to performing multivariate analysis techniques. Dynamic time warping was originally developed as a method for spoken word recognition, but shows potential in the objective analysis of time variant signals, such as manufacturing data. In this work we will discuss the application of dynamic time warping with a derivative weighting function to align chromatograms to facilitate process monitoring and fault detection. In addition, we will demonstrate the utility of this method as a preprocessing step for multivariate model development. (c) 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29: 394402, 2013

关键词： chromatography chromatogram alignment time warping multivariate statistical process control batch monitoring

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multivariate control chart based on the highest possibility region

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JOURNAL OF APPLIED STATISTICS 2013年第8期40卷 1673-1681页

作者： Gang, Ting-Ting Yang, Jun Zhao, Yu Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

The T-2 control chart is widely adopted in multivariate statistical process control. However, when dealing with asymmetrical or multimodal distributions using the traditional T-2 control chart, some points with relatively high occurrence possibility might be excluded, while some points with relatively low occurrence possibility might be accepted. Motived by the thought of the highest posterior density credible region, we develop a control chart based on the highest possibility region to solve this problem. It is shown that the proposed multivariate control chart will not only meet the false alarm requirement, but also ensure that all the in-control points are with relatively high occurrence possibility. The advantages and effectiveness of the proposed control chart are demonstrated by some numerical examples in the end.

关键词： multivariate statistical process control false alarm highest possibility region asymmetrical distributions multimodal distributions T-2 control chart

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VARIABLE SAMPLE SIZE AND SAMPLING INTERVAL HOTELLING'S T-2 CHARTS WITH RUNS RULES FOR SWITCHING BETWEEN SAMPLE SIZES AND SAMPLING INTERVAL LENGTHS

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INTERNATIONAL JOURNAL OF RELIABILITY QUALITY & SAFETY ENGINEERING 2013年第4期20卷 1350015-1350030页

作者： Mahadik, Shashibhushan B. Solapur Univ Dept Stat Solapur 413255 India

Variable sample size and sampling interval (VSSI) T-2 charts are substantially more efficient than static T-2 charts. However, the frequent switches between sample sizes and sampling interval lengths can be a complicating factor during the implementation of these charts. In this paper, runs rules are proposed for switching between sample sizes and sampling interval lengths of VSSI T-2 charts in order to reduce the frequency of switches. The expressions for performance measures for the charts with these runs rules are developed. The effects of different runs rules on performances of the charts are evaluated numerically. In general, runs rules substantially reduce the frequency of switches. However, some runs rules significantly affect statistical performances of the charts.

关键词： Adaptive control chart average time to signal average number of samples to signal average number of switches to signal multivariate statistical process control

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multivariate statistical process control charts: An overview

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2007年第5期23卷 517-543页

作者： Bersimis, S. Psarakis, S. Panaretos, J. Athens Univ Econ & Business Dept Stat GR-10434 Athens Greece Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ Piraeus Dept Stat & Insurance Sci GR-18534 Piraeus Greece

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal. Copyright (C)2006 John Wiley & Sons, Ltd.

关键词： quality control process control multivariate statistical process control Hotelling's T-2 CUSUM EWMA PCA PLS

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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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Advances in Data-driven Monitoring Methods for Complex process

Advances in Data-driven Monitoring Methods for Complex Proce...

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3rd International Conference on Applied Mechanics, Materials and Manufacturing (ICAMMM 2013)

作者： Chen, Ru Qing Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Zhejiang Peoples R China

ISBN: (纸本)9783037858882

In modern industrial processes, effective performance monitoring and quality prediction are the key to ensure plant safety and enhance product quality. The research significance and background of process monitoring and fault diagnosis technologies are described and the current advances in data-based process monitoring methods are summed up in this paper. Then the multivariate statistical process control (MSPC) methods for process with single constraint, especially for single non-Gaussian process or nonlinear process are elaborated. As real industrial process data often show strong non-Gaussian and dynamic behaviors, study on monitoring technologies for dynamic non-Gaussian process is of great importance. Finally, some challenges such as non-Gaussian and dynamic process, fault detection and diagnosis as well as new MSPC methods are indicated.

关键词： Data-driven technique process monitoring and fault diagnosis multivariate statistical process control Dynamic and non-Gaussian process

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