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Nonlinear singular spectrum analysis and its application in multivariate statistical process monitoring
Full text linkThesis (PhD)--Stellenbosch University, 2016ENGLISH ABSTRACT: Multivariate statistical process control (MSPC) approaches based on principal component analysis (PCA), partial least squares (PLS) and related extensions are now widely used for process monitoring and diagnosis in process systems where observed correlated measurements are readily available. However, highly nonlinear (dynamic) processes pose a challenge for MSPC methods as a large set of nonlinear features are typically required to capture the underlying characteristic behaviour of the process in the absence of faults. Several extensions of basic (PCA) methods have previously been proposed to handle features such as autocorrelation in data, time-frequency localization, and nonlinearity.
In this study multivariate statistical process monitoring methods based on nonlinear singular spectrum analysis which use nonlinear principal component analysis, multidimensional scaling and kernel multidimensional scaling are proposed. More specifically, singular spectrum analysis using covariance and dissimilarity scale structure are proposed to express multivariate time series as the sum of identifiable components whose basis functions are obtained from the process measurements. Such an approach is useful for extracting trends, harmonic patterns and noise in time series data. Using nonlinear SSA decomposition of time series data, a multimodal representation is obtained that can be used together with existing statistical process control methods to develop novel process monitoring schemes.
The advantages of these approaches are demonstrated on simulated multivariate nonlinear data and compared with those of classical PCA and multimodal SSA on base metal flotation plant data and the Tennessee Eastman process benchmark data. The nonlinear SSA methods better captured the nonlinearities in the observed data. Consequently, this yielded improved detection rates for various faults in nonlinear data over those obtainable by alternative competing multivariate methods.AFRIKAANSE OPSOMMING: Meerveranderlike statistiese prosesbeheer (MSP) benaderings gebaseer op hoofkomponentontleding, gedeeltelike kleinste kwadrate en verwante uitbreidings, word tans wyd gebruik in prosesmonitering en –diagnose van prosesstelsels waar waargenome gekorreleerde metings geredelik beskikbaar is. Hoogs nie-lineêre (dinamiese) prosesse is egter ’n uitdaging vir MSP metodes, aangesien ’n groot stel nie-lineêre kenmerke tipies benodig word om die onderliggende karakteristieke gedrag van die proses vas te vang in die afwesigheid van foute. Verskeie uitbreidings van basiese hoofkomponentonledingsmetodes is voorheen voorgestel om kenmerke, soos outokorrelasie, tyd-frekwensielokalisering en nie-lineariteit in die data te hanteer.
In die studie, word meerveranderlike statistiese prosesmoniteringsmetodes voorgestel, gebaseer op nie-lineêre enkelvoudige spektrumontleding wat nie-lineêre hoofkomponentontleding, meerdimensionele skalering en kern- multidimensionele skalering gebruik. Meer spesifiek, enkelvoudige spektrumontleding wat kovariansie- en andersheidskaalstrukture gebruik, word voorgestel om meerveranderlike tydreekse uit te druk as die som van identifiseerbare komponente, wat se basisfunksies van prosesmetings verkry kan word. So ’n benadering is nuttig vir die ekstraksie van tendense, harmoniese patrone en geraas in die tydreeksdata. Deur nie-lineêre enkelvoudige spektrumontleding te gebruik vir ontbinding van die tydreeksdata, word ’n multimodale verteenwoordiging verkry wat gebruik kan word saam met bestaande statistiese prosesbeheermetodes om nuwe prosesmoniteringskemas te ontwikkel.
Die voordele van die benaderings word gedemonstreer en vergelyk met die van klassieke hoofkomponentontleding en multimodale nie-lineêre enkelvoudige spektrumontleding op gesimuleerde meerveranderlike nie-lineêre data, data van ’n basismetaalflottasie-aanleg en die Tennessee Eastman prosesykingsdata. Die nie-lineêre enkelvoudige spektrumontledingsmetodes het die nie-lineariteite in die waargenome prosesdata beter beskryf. Gevolglik het dit tot beter foutopsporingstempo’s gelei, as wat behaal kon word met alternatiewe kompterende meerveranderlike metodes.
Stellenbosch University https://scholar.sun.ac.zaDoctora
Multiscale process monitoring with singular spectrum analysis
Full text linkThesis (MScEng (Process Engineering))--University of Stellenbosch, 2010.Thesis presented in partial fulfilment of the requirements for the degree
of
Master of Science in Engineering
(Extractive Metallurgy)
In the Department of Process Engineering
at the University of StellenboschENGLISH ABSTRACT: Multivariate statistical process control (MSPC) approaches are now widely used for performance monitoring, fault detection and diagnosis in chemical processes. Conventional MSPC approaches are based on latent variable projection methods such as principal component analysis and partial least squares. These methods are suitable for handling linearly correlated data sets, with minimal autocorrelation in the variables. Industrial plant data invariably violate these conditions, and several extensions to conventional MSPC methodologies have been proposed to account for these limitations.
In practical situations process data usually contain contributions at multiple scales because of different events occurring at different localizations in time and frequency. To account for such multiscale nature, monitoring techniques that decompose observed data at different scales are necessary. Hence the use of standard MSPC methodologies may lead to unreliable results due to false alarms and significant loss of information.
In this thesis a multiscale methodology based on the use of singular spectrum analysis is proposed. Singular spectrum analysis (SSA) is a linear method that extracts information from the short and noisy time series by decomposing the data into deterministic and stochastic components without prior knowledge of the dynamics affecting the time series. These components can be classified as independent additive time series of slowly varying trend, periodic series and aperiodic noise. SSA does this decomposition by projecting the original time series onto a data-adaptive vector basis obtained from the series itself based on principal component analysis (PCA).
The proposed method in this study treats each process variable as time series and the autocorrelation between the variables are explicitly accounted for. The data-adaptive nature of SSA makes the proposed method more flexible than other spectral techniques using fixed basis functions. Application of the proposed technique is demonstrated using simulated, industrial data and the Tennessee Eastman Challenge process. Also, a comparative analysis is given using the simulated and Tennessee Eastman process. It is found that in most cases the proposed method is superior in detecting process changes and faults of different magnitude accurately compared to classical statistical process control (SPC) based on latent variable methods as well as the wavelet-based multiscale SPC.AFRIKAANSE OPSOMMING: Meerveranderlike statistiese prosesbeheerbenaderings (MSPB) word tans wydverspreid benut vir werkverrigtingkontrolering, foutopsporing en .diagnose in chemiese prosesse. Gebruiklike MSPB word op latente veranderlike projeksiemetodes soos hoofkomponentontleding en parsiele kleinste-kwadrate gebaseer. Hierdie metodes is geskik om lineer gekorreleerde datastelle, met minimale outokorrelasie, te hanteer. Nywerheidsaanlegdata oortree altyd hierdie voorwaardes, en verskeie MSPB is voorgestel om verantwoording te doen vir hierdie beperkings.
Prosesdata afkomstig van praktiese toestande bevat gewoonlik bydraes by veelvuldige skale, as gevolg van verskillende gebeurtenisse wat by verskillende lokaliserings in tyd en frekwensie voorkom. Kontroleringsmetodes wat waargenome data ontbind by verskillende skale is nodig om verantwoording te doen vir sodanige multiskaalgedrag. Derhalwe kan die gebruik van standaard-MSPB weens vals alarms en beduidende verlies van inligting tot onbetroubare resultate lei.
In hierdie tesis word . multiskaalmetodologie gebaseer op die gebruik van singuliere spektrumontleding (SSO) voorgestel. SSO is . lineere metode wat inligting uit die kort en ruiserige tydreeks ontrek deur die data in deterministiese en stochastiese komponente te ontbind, sonder enige voorkennis van die dinamika wat die tydreeks affekteer. Hierdie komponente kan as onafhanklike, additiewe tydreekse geklassifiseer word: stadigveranderende tendense, periodiese reekse en aperiodiese geruis. SSO vermag hierdie ontbinding deur die oorspronklike tydreeks na . data-aanpassende vektorbasis te projekteer, waar hierdie vektorbasis verkry is vanaf die tydreeks self, gebaseer op hoofkomponentontleding.
Die voorgestelde metode in hierdie studie hanteer elke prosesveranderlike as . tydreeks, en die outokorrelasie tussen veranderlikes word eksplisiet in berekening gebring. Aangesien die SSO metode aanpas tot data, is die voorgestelde metode meer buigsaam as ander spektraalmetodes wat gebruik maak van vaste basisfunksies. Toepassing van die voorgestelde tegniek word getoon met gesimuleerde prosesdata en die Tennessee Eastman-proses. . Vergelykende ontleding word ook gedoen met die gesimuleerde prosesdata en die Tennessee Eastman-proses. In die meeste gevalle is dit gevind dat die voorgestelde metode beter vaar om prosesveranderings en .foute met verskillende groottes op te spoor, in vergeleke met klassieke statistiese prosesbeheer (SP) gebaseer op latente veranderlikes, asook golfie-gebaseerde multiskaal SP
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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