Computers and Chemical Engineering 48 (2013) 187199
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Computers and Chemical Engineering
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ualitative Representation of Trends (QRT): Extended method for identificationf consecutive inflection points
ris Villeza,b,, Christian Rosnc, Francois Anctild, Carl Duchesnee, Peter A. Vanrolleghema,d
BIOMATH, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, BelgiumLaboratory for Intelligent Process Systems, School of Chem. Eng., Purdue University, 480 Stadium Mall Drive, West Lafayette, IN 47907, USAVeolia Water Solutions & Technology, AnoxKaldnes AB, Klosterngsvgen 11A, SE-226 47 Lund, SwedenDpartement de gnie civil et de gnie des eaux, Universit Laval, 1065 av. de la Mdecine, Qubec, QC G1V 0A6, CanadaDpartement de gnie chimique, Universit Laval, 1065 av. de la Mdecine, Qubec, QC G1V 0A6, Canada
r t i c l e i n f o
rticle history:eceived 6 September 2011eceived in revised form 3 May 2012ccepted 14 August 2012vailable online 8 September 2012
a b s t r a c t
In this paper a methodology for extraction of qualitative descriptions of data series is extended for properinflection point recognition. The original method allows the extraction of qualitative information suchas the sign of the first and second derivatives of the assumed signals underlying univariate noisy timeseries. However, it is not able to handle consecutive inflection points, i.e. inflection points which followeach other in time with no minimum or maximum in between them. To improve this method for proper
eywords:ualitative Trend Analysis (QTA)ualitative Representation of Trends (QRT)ata miningualitative analysis
assessment of inflection points, the original method is compared with three modifications of the originalmethod. One of the alternatives based on repeated application of Witkins stability criterion deliversbetter results for both identification of the qualitative descriptions and the locations in time of extremaand inflection points. Furthermore, the same modified method is shown to deliver the best fault diagnosisperformance in a benchmark batch fermentation study. Furthermore, the same modified method is shownto deliver the best fault diagnosis performance in a benchmark batch fermentation study.
Qualitative interpretation of time series has received fair atten-ion in literature over the past 15 years. The use of qualitativenformation is often warranted and supported by the observationhat an operators reasoning or knowledge is to a large extent basedn qualitative features in data series rather than their quantita-ive properties. Given that operators spend a large proportion ofheir time to the monitoring of trends in process measurementsYamanaka & Nishiya, 1997), an automated assessment of processrends can facilitate operators in performing the task of processupervision. Part of the potential for such a tool lies in the con-ext of fault detection and identification (Venkatasubramanian,engaswamy, & Kavuri, 2003; Villez, Keser, & Rieger, 2009; Villez,osn, Anctil, Duchesne, & Vanrolleghem, 2008). Indeed, informa-ion about trends may be addressed to the operator only in casef abnormalities. As such, an operator does not need to check nor-
al data on a regular basis and can focus on true problems. This
educes the time spent on evaluation of normal data and will likelyesult in a faster analysis and reaction to abnormal situations. More
Corresponding author at: Eawag, berlandstrasse 133, P. O. Box 611, 8600bendorf, Switzerland.
E-mail address: firstname.lastname@example.org (K. Villez).
098-1354/$ see front matter 2012 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.compchemeng.2012.08.010
2012 Elsevier Ltd. All rights reserved.
advanced use of such tools may include the design of automateddiagnosis and/or control systems that are based on qualitativeassessment of process trends. Furthermore, methods focused onqualitative features are well-suited for data mining of process data(Villez et al., 2007).
In the context of automated control of biological wastewa-ter treatment plants for nutrient removal, oxidation reductionpotential (ORP) signals and the points within time series thereofwhere transitions from accelerating to decelerating behavior andvice versa occur (inflection points) have been addressed as keyindicators (Andreottola, Foladori, & Ragazzi, 2001; Fuerhackeret al., 2001; Kim, Chen, Kishida, & Sudo, 2004; Li, Peng, Peng, &Wang, 2004; Plisson-Saune, Capdeville, Mauret, Deguin, & Baptiste,1996; Ra, Lo, & Mavinic, 1999; Vanrolleghem & Coen, 1995;Wareham, Mavinic, & Hall, 1994; Yu, Liaw, Chang, & Cheng,1998). As the inflection points in ORP signals can be used asindicators for the end of nitrification and denitrification pro-cesses in waste water treatment systems (Chang & Hao, 1996),an accurate assessment of inflection points with minimal delay isdesired for such applications. In other biotechnological applicationsqualitative descriptions are used for model structure identifica-
tion (Shaich, Becker, & King, 2001; Vanrolleghem & Van Daele,1994).
Methods available for the assessment of Qualitative Represen-tation of Trends can be divided into two main classes, based on the
88 K. Villez et al. / Computers and Ch
rinciples of their core methodology. The first group is based onhe training of a clustering (unsupervised) or classifier (supervisedearning) model. Wang and Li (1999) use PCA-based clustering
hile Rengaswamy and Venkatasubramanian (1995) use neuraletworks. An essential characteristic and potential weakness ofhese methods is the necessity for training of the models on his-orical data prior to application. This may lead to errors related toxtrapolation to new data for which the models are not trained.ethods of the second class do not require such explicit training
nd can be applied directly to any time series. Most of these areased on the fitting of polynomial functions in contiguous win-ows. A prior assessment of one or more parameters (e.g. noise
evel) for acceptable fits is however essential. This requirement lim-ts the use of the techniques in case of changing noise characteristicsnd may lead to errors due to extrapolation as well. The meth-ds of Charbonnier, Garcia-Beltan, Cadet, and Gentil (2005), Dash,aurya, Venkatasubramanian, and Rengaswamy (2004), Flehmig,atzdorf, and Marquardt (1998) share this characteristic. Within
his second class, an exception to this is only found with the methodxplained by Bakshi and Stephanopoulos (1994) which does notequire an assessment of noise characteristics. It is the latter Qual-tative Representations of Trends (QRT) method and modificationshereof which are the subject of this study.
Qualitative analysis of time series has been judged promisingo solve many problems, including data mining, fault detectionnd identification (FDI) and reaction end-point detection. How-ver, effective and critical comparison of the available techniquess very limited. For this reason, this study includes a first bench-
arking study in qualitative analysis. The original QRT methodnd the proposed modification are compared in terms of accuracy,omputational demand and fault detection and identification (FDI)erformance.
It is safe to say that the selected method, called Qualitativeepresentation of Trends (QRT), is fairly complicated. It servesherefore to evaluate what is favorable about this method. Firstf all, neither model training or assessment of statistical propertiesf the signal (e.g. noise level) are necessary prior to application ofhe method. This advantage results from the use of a feature selec-ion rule, called Witkins stability criterion (Witkin, 1983), which isimed at the discrimination between noisy and acceptable qualita-ive features in a time series. This heuristic rule is based on thentuitive notion that true or meaningful qualitative features are
ore difficult to filter from a time series than noisy ones. Its appli-ation results in implicit control of the complexity of the resultingualitative representation. Because of the heuristic, it is a veryeneric method which requires little tuning or adjustment oncemplemented. Second, the QRT method is based on the cubic spline
avelet decomposition which means that the method implic-tly assumes continuity of the first and second derivatives of thessumed signal underlying the time series. This was a fair assump-ion to make in the context of the original study (Villez, 2007),s the focus lay on the analysis of pH and oxidationreductionotential time series which were not characterized by visible dis-ontinuities. Third, an initial comparison with the Qualitative Trendnalysis method, proposed by Dash et al. (2004), indicated someroblems with the QTA method that were considered prohibitiveor application for the particular case study. For example, a pre-iminary comparison (Villez, 2007) between the QTA and QRT
ethod indicated that the QTA method as implemented does notnforce continuity of the first and second derivatives. In addition,he same noise-free signal resulted in a large variability of qualita-ive descriptions when repeated for different noise sequences. The
riginal QRT meth