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multivariate statistical process control based on multiway locality preserving projections

JOURNAL OF process control 2008年第7-8期18卷 797-807页

作者： Hu, Kunlun Yuan, Jingqi Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data seta It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling's T-2 (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： locality preserving projections multivariate statistical process control batch process monitoring fault detection and diagnosis moving window technique robustness

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A new proposal to solve the autocorrelation problem for monitoring processes in the cutlery industry

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JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS 2014年第2期31卷 323-333页

作者： Alfaro Navarro, Jose Luis Vargas Vargas, Manuel Andres Martinez, Maria Encarnacion Mondejar Jimenez, Jose Univ Castilla La Mancha Fac Ciencias Econ & Empresariales Albacete Albacete 02071 Spain Univ Castilla La Mancha Fac Ciencias Sociales Cuenca Cuenca 16071 Spain

Traditional multivariate control charts consider the observations to be independent. In many industrial applications, however, a certain autocorrelation level can normally be observed. This can be due to the nature of the process itself (inertia in the process), or to the way in which the sample is obtained. In the latter case, the problem involved in the autocorrelation existence was treated by the individual modelling of each series or the application of vector autoregression (VAR) models. In the former case, the analysis of the cross correlation structure between the variables is altered. In the latter one, if the cross correlation is not strong, the filtering process may modify the weakest relations. In order to improve these aspects, state-space models have been introduced into multivariate statistical process control. In the present paper, we consider a cutlery industry in order to develop a complete scheme for the efficient construction of control charts for innovations and the "inertial factor". This is the main difference from other proposals in the literature;that is, consideration of the inertial factor in the statistical process control.

关键词： multivariate statistical process control control charts Autocorrelation State-space models Inertial factor

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An error-in-variable projection to latent structure framework for monitoring technical systems with orthogonal signal components

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2014年 133卷 70-83页

作者： Li, Zhe Kruger, Uwe Wang, Xun Xie, Lei Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Zhejiang Peoples R China Sultan Qaboos Univ Dept Mech & Ind Engn Muscat Oman Petr Inst Dept Math Abu Dhabi U Arab Emirates

This article introduces an error-in-variable (EIV) data structure for process monitoring models, that assumes the presence of source variables that can be correlated and uncorrelated to the system response variables. To identify such models, the paper proposes a different objective function for the projection to latent structure (PLS) approach. Compared to existing work, this modified PLS formulation does not remove uncorrelated, or orthogonal, components from the predictor set prior to the identification of a PLS model nor identify an initial PLS model and carry out a subsequent extraction of various correlated and orthogonal components from the predictor set. The proposed PLS algorithms extract latent components to predict the system response variables as accurately as possible after estimating the error covariance matrices using a maximum likelihood algorithm that is also introduced in this article. A detailed analysis of this extended PLS framework yields that the objective function is an iterative maximum redundancy formulation. The article finally shows that the developed algorithms here are computationally more efficient than existing PLS-based approaches. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Projection to latent structures Maximum redundancy Error-in-variables Maximum likelihood estimation Orthogonal signal components multivariate statistical process control

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control Charts for Monitoring Linear Profiles with Within-Profile Correlation Using Gaussian process Models

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2014年第4期30卷 487-501页

作者： Zhang, Yang He, Zhen Zhang, Chi Woodall, William H. Tianjin Univ Dept Ind Engn Tianjin 300072 Peoples R China Tianjin Univ Dept Management Sci & Engn Tianjin 300072 Peoples R China Virginia Tech Dept Stat Blacksburg VA 24061 USA

Profile monitoring is the utilization of control charts for checking the stability of the quality of a product over time when the product quality is characterized by a function at each time point. Most existing control charts for monitoring profiles are based on the assumption that the observations within each profile are independent of each other, which is often invalid in practice. Successive measurements within profiles often exhibit spatial or serial correlation. This paper focuses on Phase II linear profile monitoring when within-profile data are correlated. A Gaussian process model is used to describe the within-profile correlation (WPC). Two Shewhart-type multivariate control charts are proposed to monitor the linear trend term and the WPC separately in Phase II. Our proposed approaches are compared with alternative methods through numerical simulations in which different in-control WPCs are considered. Simulation studies show that the proposed control charts are sensitive to changes in the linear trend term when the correlation is strong and effective in detecting large shifts in the WPC. Finally, an example is given to illustrate the implementation of our proposed control charts. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： average run length linear regression multivariate statistical process control Phase II squared exponential covariance function

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Bilinear modeling of batch processes. Part III: parameter stability

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JOURNAL OF CHEMOMETRICS 2014年第1期28卷 10-27页

作者： Maria Gonzalez-Martinez, Jose Camacho, Jose Ferrer, Alberto Univ Politecn Valencia Dept Appl Stat Operat Res & Qual Valencia 46022 Spain Univ Granada Dept Signal Theory Networking & Commun E-18071 Granada Spain

A paramount aspect in the development of a model for a monitoring system is the so-called parameter stability. This is inversely related to the uncertainty, i.e., the variance in the parameters estimates. Noise affects the performance of the monitoring system, reducing its fault detection capability. Low parameters uncertainty is desired to ensure a reduced amount of noise in the model. Nonetheless, there is no sound study on the parameter stability in batch multivariate statistical process control (BMSPC). The aim of this paper is to investigate the parameter stability associated to the most used synchronization and principal component analysis-based BMSPC methods. The synchronization methods included in this study are the following: indicator variable, dynamic time warping, relaxed greedy time warping, and time linear expanding/compressing-based. In addition, different arrangements of the three-way batch data into two-way matrices are considered, namely single-model, K-models, and hierarchical-model approaches. Results are discussed in connection with previous conclusions in the first two papers of the series. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： stability uncertainty multivariate statistical process control unfolding principal component analysis synchronization

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Inter-population variation on the accumulation and translocation of potentially harmful chemical elements in Cistus ladanifer L. from Brancanes, Caveira, Chan‡a, Lousal, Neves Corvo and So Domingos mines in the Portuguese Iberian Pyrite Belt

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JOURNAL OF SOILS AND SEDIMENTS 2014年第4期14卷 758-772页

作者： Santos, Erika S. Abreu, Maria Manuela Batista, Maria Joao Magalhes, M. Clara F. Fernandes, Eliana Inst Super Dom Afonso III Ctr Invest Ciencias Ambiente & Empresariai P-8100641 Convento Espirito Santo Loule Portugal Univ Lisbon Inst Super Agron Unidade Invest Quim Ambiental P-1349017 Lisbon Portugal Lab Nacl Energia & Geol P-2721866 Alfragide Portugal Univ Aveiro Dept Quim Ctr Invest Mat Ceram & Compositos CICECO P-3810193 Aveiro Portugal

This study aimed to compare the variation on the accumulation and translocation of potentially harmful chemical elements and nutrients (As, Ca, Cu, Fe, K, Mg, Mn, Ni, Pb and Zn) in Cistus ladanifer L. belonging to populations growing in different mine areas from the Portuguese Iberian Pyrite Belt (Brancanes, Caveira, Chan double dagger a, Lousal, Neves Corvo, So Domingos). These mines are abandoned (except Neves Corvo that is still operating) and have different contamination levels. Composite samples of soils (n = 31), developed on different mine wastes and/or host rock, and C. ladanifer plants (roots and shoots) were collected in the mine areas. Soils were characterized for pH, NPK and organic C, by classical methodologies. Soils (total fraction-four acid digestion, and available fraction-extracted with aqueous solution of diluted organic acids, simulating rizosphere conditions) and plants (ashing followed by acid digestion) elemental concentrations were determined by ICP. Soil-plant transfer and translocation coefficients were calculated. Principal components analysis in both ways, the classical method and a second approach with adaptations used mostly in multivariate statistical processes control data, were done in order to compare the plants populations. Soils had large heterogeneity in their characteristics. Caveira, Lousal, Neves Corvo and So Domingos soils showed the highest total concentrations of As, Cu, Pb and Zn. Independently of the mine, available fractions of elements were low. Intra- and inter-population variations in accumulation and translocation of elements were evaluated. Plants were not accumulators of the majority of the analysed elements. Nutrients were mainly translocated from roots to shoots, while trace elements were stored in roots (except in Neves Corvo for As and Pb, and So Domingos for As). Elements concentrations in plant populations from Lousal, Chan double dagger a and So Domingos did not present much variation. Brancanes soils and pl

关键词： Iberian Pyrite Belt multivariate statistical process control Nonlinear Iterative Partial Least Square (NIPALS) Portugal Principal components analysis

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Development of Drowsy Driving Accident Prediction by Heart Rate Variability Analysis

Development of Drowsy Driving Accident Prediction by Heart R...

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Annual Summit and Conference of Asia-Pacific-Signal-and-Information-processing-Association (APSIPA)

作者： Abe, Erika Fujiwara, Koichi Hiraoka, Toshihiro Yamakawa, Toshitaka Kano, Manabu Kyoto Univ Dept Syst Sci Kyoto Japan Kumamoto Univ Prior Org Innovat & Excellence Kumamoto Japan Kumamoto Univ Dept Comp Sci & Elect Engn Kumamoto Japan

ISBN: (纸本)9786163618238

Drowsy driving accidents can be prevented if it can be predicted in advance. The present work aims to develop a new method for predicting a drowsy driving accident based on the fact that the autonomic nervous function affects heart rate variability (HRV), which is the fluctuation of the RR interval (RRI) obtained from an electrocardiogram (ECG). The proposed method uses HRV features derived through HRV analysis as input variables of multivariate statistical process control (MSPC), which is a well-known anomaly detection method in process control. Driving simulator experiments demonstrated that driver drowsiness was successfully predicted seven out of eight cases before drowsy driving accidents occur.

关键词： accident prevention electrocardiography medical signal detection neurophysiology statistical process control ECG HRV analysis MSPC RR interval anomaly detection method autonomic nervous function driver drowsiness drowsy driving accident prediction electrocardiogram heart rate variability analysis multivariate statistical process control Accidents Data models Feature extraction Heart rate variability Monitoring process control Rail to rail inputs

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Dynamic analysis of on-line high-performance liquid chromatography for multivariate statistical process control

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JOURNAL OF CHROMATOGRAPHY A 2008年第2期1213卷 130-144页

作者： Kittiwachana, Sila Ferreira, Diana L. S. Fido, Louise A. Thompson, Duncan R. Escott, Richard E. A. Brereton, Richard G. Univ Bristol Sch Chem Ctr Chemometr Bristol BS8 1TS Avon England GlaxoSmithKline Inc Stevenage SG1 2NY Herts England GlaxoSmithKline Inc Tonbridge TN11 9AN Kent England

A continuous process was studied over 83.32 h using on-line high-performance liquid chromatography, involving the acquisition of 252 chromatograms. A method for analysis of these data using multivariate statistical process control on peak tables, in real-time, is described. The normal operating condition (NOC) region of the process was identified using evolving principal components analysis to be between 5.77 and 8,13 h.19 out of the 37 peaks detected throughout the process were found in the NOC region, the remainder representing undesirable contaminants found elsewhere in the process. A major challenge is to develop the peak table as the process evolves, which is dynamically updated as new peaks are detected after the NOC region: this approach involving an "unlocked" peak table is contrasted to an approach using a "locked" peak table where only peaks detected during the NOC region are included in the model. In addition, results are compared to those obtained using baseline corrected and aligned chromatograms, using a NOC region of 5.85-8.33 h. D- and Q-charts were obtained. It is shown that the "unlocked" peak table detects out of control samples best and provides good diagnostic insight into problems with the process. (c) 2008 Elsevier B.V. All rights reserved.

关键词： High-performance liquid chromatography multivariate statistical process control Continuous processes Reaction monitoring

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Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors

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process BIOCHEMISTRY 2008年第10期43卷 1107-1113页

作者： Lee, Min Woo Hong, Sung Hun Choi, Hyeoksun Kim, Ji-Hoon Lee, Dae Sung Park, Jong Moon Kyungpook Natl Univ Dept Environm Engn Taegu 702701 South Korea Pohang Univ Sci & Technol Dept Chem Engn Pohang 790784 Gyeongbuk South Korea Pohang Univ Sci & Technol Sch Environm Sci & Engn Pohang 790784 Gyeongbuk South Korea

A real-time remote monitoring system for wastewater treatment plants (WWTPs) has been developed to give local operators a guideline that would allow them to arrive at the optimum operational strategy in the early stage of a process disturbance. Especially, small-scaled WWTPs in Korea's rural areas show a large fluctuation in their influent loading and, therefore, they require an efficient operation for treatment of organic matter, nitrogen and phosphorus. However, under requirements to lower running costs, most of the small-scaled WWTPs are being forced to operate with a minimum number of operators. It is too costly for them to employ a local expert to maintain plant systems properly. In recent years, recognition of these problems has raised great interests in real-time remote monitoring systems. They serve the key information needed for efficient operation, and help to transfer knowledge from the experts at a remote control center to local operators in real-time. In this study, both operation data and measurement values from a novel mobile multi-sensor system were transmitted on-line by a telecommunication system. Then multivariate statistical process controls and software sensor techniques were applied to supervise local WWTPs. The developed remote monitoring system makes it possible to monitor the current plants' statuses and to support the operation of local wastewater systems. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： remote monitoring multivariate statistical process control principal component analysis neural network software sensor wastewater treatment plant

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Improvement of causal analysis using multivariate statistical process control

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SOFTWARE QUALITY JOURNAL 2008年第3期16卷 377-409页

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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