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Some properties of a linear combination CUSUM statistic for controlling a multivariate mean vector

JOURNAL OF statistical PLANNING AND INFERENCE 2007年第11期137卷 3591-3600页

作者： Li, Ruojia Johnson, Richard A. Cho, Sang-Hoon Univ Wisconsin Dept Stat Madison WI 53706 USA Eli Lilly & Co Lilly Corp Ctr Global Stat Sci Indianapolis IN 46285 USA

We develop a class of new multivariate procedures for monitoring quality by detecting a change in the level of a multivariate process. Following the ideas of S.N. Roy, we first consider a linear combination statistic which results from projecting the multivariate observations onto a unit vector and then maximizing a selected univariate statistic over all directions. An application to a sheet metal assembly process is discussed in Section 3. Comparisons are made between one of our new procedures and other major multivariate quality monitoring schemes in Section 4. A small simulation study compares the average run lengths of the different procedures both when the process is in-control and after a shift has occurred. Some large sample properties are discussed in Section 5 and some computational details in Section 6. (C) 2007 Elsevier B.V. All rights reserved.

关键词： average run length (ARL) multivariate statistical process control

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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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Examples of NIR based real time release in tablet manufacturing

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JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS 2007年第4期43卷 1297-1305页

作者： Skibsted, E. T. S. Westerhuis, J. A. Smilde, A. K. Witte, D. T. Univ Amsterdam Swammerdam Inst Life Sci NL-1018 WV Amsterdam Netherlands Novo Nordisk AS PMC Anal DK-2760 Malov Denmark NV Organon NL-5340 BH Oss Netherlands

Real time release (RTR) of products is a new paradigm in the pharmaceutical industry. An RTR system assures that when the last manufacturing step is passed all the final release criteria are met. Various types of models can be used within the RTR framework. For each RTR system, the monitoring capability, control capability and RTR capability need to be tested. This paper presents some practical examples within the RTR framework using near-infrared and process data obtained from a tablet manufacturing process. (c) 2006 Elsevier B.V. All rights reserved.

关键词： real time release near-infrared process models chemometrics process analytical technology multivariate statistical process control regression models tablet manufacturing

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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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Profile Monitoring via Nonlinear Mixed Models

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JOURNAL OF QUALITY TECHNOLOGY 2009年第1期41卷 18-34页

作者： Jensen, Willis A. Birch, Jeffrey B. WL Gore & Assoc Inc Med Prod Div Flagstaff AZ 86003 USA Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA

Profile monitoring is a relatively new technique in quality control best used where the process data follow a profile (or curve) at each time period. Little work has been done on the monitoring of nonlinear profiles. Previous work has assumed that the measurements within a profile are uncorrelated. To relax this restriction, we propose the use of nonlinear mixed models to monitor the nonlinear profiles in order to account for the correlation structure. We evaluate the effectiveness of fitting separate nonlinear regression models to each profile in Phase I control chart applications for data with uncorrelated errors and no random effects. For data with random effects, we compare the effectiveness of charts based on a separate nonlinear regression approach versus those based on a nonlinear mixed model approach. Our proposed approach uses the separate nonlinear regression model fits to obtain a nonlinear mixed model fit. Our studies show the nonlinear mixed model approach to be clearly superior to fitting separate nonlinear regression models. As a consequence, the nonlinear mixed model approach results in charts with good abilities to detect changes in Phase I data and has a simple-to-calculate control limit.

关键词： Eblups multivariate statistical process control Nonlinear Model Phase I T-2 Statistic

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Potential of multivariate statistical process monitoring based on the biogas composition to detect free ammonia intoxication in anaerobic reactors

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BIOCHEMICAL ENGINEERING JOURNAL 2018年 140卷 17-28页

作者： Lemaigre, Sebastien Adam, Gilles Gerin, Patrick A. Noo, Anais De Vos, Benedicte Klimek, Dominika Goux, Xavier Calusinska, Magdalena Delfosse, Philippe LIST Environm Res & Innovat Dept ERIN Rue Brill 41 L-4422 Belvaux Luxembourg Catholic Univ Louvain Earth & Life Inst Bioengn Croix Sud 2Box L7-05-19 B-1348 Louvain La Neuve Belgium

Three anaerobic digestion reactors were initially maintained at steady-state with low nitrogen input, then progressively exposed to increasing nitrogen input until extreme ammonia intoxication, leading to interruption of biogas production. Such interruption occurred for a free ammonia nitrogen (FAN) concentration of about 5 g L-slurry(-1). A fourth reactor used as a control was maintained at low nitrogen steady-state. The biogas composition (CH4, CO2, H-2 and H2S) was recorded over time and data were fed into a multivariate statistical process control model based on principal components analysis (PCA-MSPC). Static PCA-MSPC failed to distinguish between the actual reactor performance decrease and the drift of the in-control process baseline. However, recursively updated PCA-MSPC (effective memory length of 14 days, i.e. 0.25 hydraulic retention time) allowed detection of the early signs of FAN induced process dysfunction, which were simultaneous with drastic changes in the microbiome of the replicate reactors. However, the process control model was not able to adapt to a drastic modification of the microbiome. Propionate continuously accumulated since the detection of the first signs of process dysfunction.

关键词： Anaerobic digestion Ammonia intoxication multivariate statistical process control Monitoring model Principal components analysis Biogas composition

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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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Detection and identification of mean shift using independent component analysis in multivariate processes

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JOURNAL OF statistical COMPUTATION AND SIMULATION 2022年第9期92卷 1920-1940页

作者： Guler, Zumre Ozdemir Bakir, Mehmet Akif Karamanoglu Mehmetbey Univ Dept Econometr TR-70200 Karaman Turkey Gazi Univ Dept Stat Ankara Turkey

multivariate statistical process control (MSPC) methods are used commonly to detect and identify shifts in multivariate industrial processes. However, these methods are limited by assumptions and complexities. In this study, a new two-stage MSPC approach based on independent component analysis is proposed. The proposed method aims to provide solutions to both the determination and the identification of the shift in the mean vector of a multivariate process. In the first step of this new method, independent components extracted by ICA were used as monitoring statistics in the MSPC chart to detect the shift in the process. The second step of the method started to deal with the problem of identifying the source of this shift by decomposing the monitoring statistics. The simulation results show the superiority of the new method over traditional methods in both determining and identifying the shift in the process mean vector.

关键词： multivariate statistical process control independent component analysis shift detection shift identification control chart

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Dynamic Non-parametric Monitoring of Air-Pollution

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2020年第4期22卷 1457-1479页

作者： Bersimis, Sotiris Triantafyllopoulos, Kostas Univ Piraeus Dept Stat & Insurance Sci 80 Karaoli & Dimitriou St Piraeus 18534 Greece Univ Sheffield Sch Math & Stat Hicks Bldg Sheffield S3 7RH S Yorkshire England

Air pollution poses a major problem in modern cities, as it has a significant effect in poor quality of life of the general population. Many recent studies link excess levels of major air pollutants with health-related incidents, in particular respiratory-related diseases. This introduces the need for city pollution on-line monitoring to enable quick identification of deviations from "normal" pollution levels, and providing useful information to public authorities for public protection. This article considers dynamic monitoring of pollution data (output of multivariate processes) using Kalman filters and multivariate statistical process control techniques. A state space model is used to define the in-control process dynamics, involving trend and seasonality. Distribution-free monitoring of the residuals of that model is proposed, based on binomial-type and generalised binomial-type statistics as well as on rank statistics. We discuss the general problem of detecting a change in pollutant levels that affects either the entire city (globally) or specific sub-areas (locally). The proposed methodology is illustrated using data, consisting of ozone, nitrogen oxides and sulfur dioxide collected over the air-quality monitoring network of Athens.

关键词： multivariate statistical process control Time series monitoring Air surveillance Air pollution Non-parametric control chart Generalised binomial-type statistics Markov chain embedded variables Primary Secondary

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Improved monitoring of batch processes by incorporating external information

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JOURNAL OF process control 2002年第4期12卷 569-576页

作者： Ramaker, HJ van Sprang, ENM Gurden, SP Westerhuis, JA Smilde, AK Univ Amsterdam Dept Chem Engn NL-1018 WV Amsterdam Netherlands

In this paper an overview is given of statistical process monitoring with the emphasis on batch processes and the possible steps to take for improving this by incorporating external information. First, the general concept of statistical process monitoring of batches is explained. This concept has already been shown to be successful according to the number of references to industrial applications. The performance of statistical process monitoring of batch processes can be enhanced by incorporating external information. Two types of external information can be distinguished: batch-run specific and process specific information. Various examples of both types of external information are given. Several ideas of how to incorporate the external information in model development are discussed. The concept of incorporating process specific information is highlighted by an example of a grey model. This model is applied to a biochemical batch process that is spectroscopically monitored. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： multivariate statistical process control batch process empirical models least-squares approximation monitoring

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