An industrial PID data repository for control loop performance monitoring (CPM)

Control loop performance monitoring methods to detect problems in PID loops are developed and tested using industrial data sets. The data is captured from the process, passed on to the researcher who tries out new detection and diagnosis methods. The data is not generally shared with other researchers working on similar problems. The authors therefore have implemented a data repository to categorise and store the data so that it becomes accessible to all researchers. Existing methods can be compared and enhanced using the data sets. This paper describes the context of CPM as well as the data repository. The repository is set up, hosted and maintained by the South African Council for Automation and Control using a professional web developer.https://www.journals.elsevier.com/ifac-papersonline2019-04-01hj2018Electrical, Electronic and Computer Engineerin

Using apparent activation energy as a reactivity criterion for biomass pyrolysis

The reactivity of chemically isolated lignocellulosic blocks, namely, α-cellulose, holocellulose, and lignin, has been rationalized on the basis of the dependence of the effective activation energy (Eα) upon conversion (α) determined via the popular isoconversional kinetic analysis, Friedman’s method. First of all, a detailed procedure for the thermogravimetric data preparation, kinetic calculation, and uncertainty estimation was implemented. Resulting Eα dependencies obtained for the slow pyrolysis of the extractive-free Eucalyptus grandis isolated α-cellulose and holocellulose remained constant for 0.05 < α < 0.80 and equal to 173 ± 10, 208 ± 11, and 197 ± 118 kJ/mol, thus confirming the single-step nature of pyrolysis. On the other hand, large and significant variations in Eα with α from 174 ± 10 to 322 ± 11 kJ/mol in the region of 0.05 and 0.79 were obtained for the Klason lignin and reported for the first time. The non-monotonic nature of weight loss at low and high conversions had a direct consequence on the confidence levels of Eα. The new experimental and calculation guidelines applied led to more accurate estimates of Eα values than those reported earlier. The increasing Eα dependency trend confirms that lignin is converted into a thermally more stable carbonaceous material

Process monitoring and fault diagnosis using random forests

Thesis (PhD (Process Engineering))--University of Stellenbosch, 2010.Dissertation presented for the Degree of DOCTOR OF PHILOSOPHY (Extractive Metallurgical Engineering) in the Department of Process Engineering at the University of StellenboschENGLISH ABSTRACT: Fault diagnosis is an important component of process monitoring, relevant in the greater context of developing safer, cleaner and more cost efficient processes. Data-driven unsupervised (or feature extractive) approaches to fault diagnosis exploit the many measurements available on modern plants. Certain current unsupervised approaches are hampered by their linearity assumptions, motivating the investigation of nonlinear methods. The diversity of data structures also motivates the investigation of novel feature extraction methodologies in process monitoring. Random forests are recently proposed statistical inference tools, deriving their predictive accuracy from the nonlinear nature of their constituent decision tree members and the power of ensembles. Random forest committees provide more than just predictions; model information on data proximities can be exploited to provide random forest features. Variable importance measures show which variables are closely associated with a chosen response variable, while partial dependencies indicate the relation of important variables to said response variable. The purpose of this study was therefore to investigate the feasibility of a new unsupervised method based on random forests as a potentially viable contender in the process monitoring statistical tool family. The hypothesis investigated was that unsupervised process monitoring and fault diagnosis can be improved by using features extracted from data with random forests, with further interpretation of fault conditions aided by random forest tools. The experimental results presented in this work support this hypothesis. An initial study was performed to assess the quality of random forest features. Random forest features were shown to be generally difficult to interpret in terms of geometry present in the original variable space. Random forest mapping and demapping models were shown to be very accurate on training data, and to extrapolate weakly to unseen data that do not fall within regions populated by training data. Random forest feature extraction was applied to unsupervised fault diagnosis for process data, and compared to linear and nonlinear methods. Random forest results were comparable to existing techniques, with the majority of random forest detections due to variable reconstruction errors. Further investigation revealed that the residual detection success of random forests originates from the constrained responses and poor generalization artifacts of decision trees. Random forest variable importance measures and partial dependencies were incorporated in a visualization tool to allow for the interpretation of fault conditions. A dynamic change point detection application with random forests proved more successful than an existing principal component analysis-based approach, with the success of the random forest method again residing in reconstruction errors. The addition of random forest fault diagnosis and change point detection algorithms to a suite of abnormal event detection techniques is recommended. The distance-to-model diagnostic based on random forest mapping and demapping proved successful in this work, and the theoretical understanding gained supports the application of this method to further data sets.AFRIKAANSE OPSOMMING: Foutdiagnose is ’n belangrike komponent van prosesmonitering, en is relevant binne die groter konteks van die ontwikkeling van veiliger, skoner en meer koste-effektiewe prosesse. Data-gedrewe toesigvrye of kenmerkekstraksie-benaderings tot foutdiagnose benut die vele metings wat op moderne prosesaanlegte beskikbaar is. Party van die huidige toesigvrye benaderings word deur aannames rakende liniariteit belemmer, wat as motivering dien om nie-liniêre metodes te ondersoek. Die diversiteit van datastrukture is ook verdere motivering vir ondersoek na nuwe kenmerkekstraksiemetodes in prosesmonitering. Lukrake-woude is ’n nuwe statistiese inferensie-tegniek, waarvan die akkuraatheid toegeskryf kan word aan die nie-liniêre aard van besluitnemingsboomlede en die bekwaamheid van ensembles. Lukrake-woudkomitees verskaf meer as net voorspellings; modelinligting oor datapuntnabyheid kan benut word om lukrakewoudkenmerke te verskaf. Metingbelangrikheidsaanduiers wys watter metings in ’n noue verhouding met ’n gekose uitsetveranderlike verkeer, terwyl parsiële afhanklikhede aandui wat die verhouding van ’n belangrike meting tot die gekose uitsetveranderlike is. Die doel van hierdie studie was dus om die uitvoerbaarheid van ’n nuwe toesigvrye metode vir prosesmonitering gebaseer op lukrake-woude te ondersoek. Die ondersoekte hipotese lui: toesigvrye prosesmonitering en foutdiagnose kan verbeter word deur kenmerke te gebruik wat met lukrake-woude geëkstraheer is, waar die verdere interpretasie van foutkondisies deur addisionele lukrake-woude-tegnieke bygestaan word. Eksperimentele resultate wat in hierdie werkstuk voorgelê is, ondersteun hierdie hipotese. ’n Intreestudie is gedoen om die gehalte van lukrake-woudkenmerke te assesseer. Daar is bevind dat dit moeilik is om lukrake-woudkenmerke in terme van die geometrie van die oorspronklike metingspasie te interpreteer. Verder is daar bevind dat lukrake-woudkartering en -dekartering baie akkuraat is vir opleidingsdata, maar dat dit swak ekstrapolasie-eienskappe toon vir ongesiene data wat in gebiede buite dié van die opleidingsdata val. Lukrake-woudkenmerkekstraksie is in toesigvrye-foutdiagnose vir gestadigde-toestandprosesse toegepas, en is met liniêre en nie-liniêre metodes vergelyk. Resultate met lukrake-woude is vergelykbaar met dié van bestaande metodes, en die meerderheid lukrake-woudopsporings is aan metingrekonstruksiefoute toe te skryf. Verdere ondersoek het getoon dat die sukses van res-opsporing op die beperkte uitsetwaardes en swak veralgemenende eienskappe van besluitnemingsbome berus. Lukrake-woude-metingbelangrikheidsaanduiers en parsiële afhanklikhede is ingelyf in ’n visualiseringstegniek wat vir die interpretasie van foutkondisies voorsiening maak. ’n Dinamiese aanwending van veranderingspuntopsporing met lukrake-woude is as meer suksesvol bewys as ’n bestaande metode gebaseer op hoofkomponentanalise. Die sukses van die lukrake-woudmetode is weereens aan rekonstruksie-reswaardes toe te skryf. ’n Voorstel wat na aanleiding van hierde studie gemaak is, is dat die lukrake-woudveranderingspunt- en foutopsporingsmetodes by ’n soortgelyke stel metodes gevoeg kan word. Daar is in hierdie werk bevind dat die afstand-vanaf-modeldiagnostiek gebaseer op lukrake-woudkartering en -dekartering suksesvol is vir foutopsporing. Die teoretiese begrippe wat ontsluier is, ondersteun die toepassing van hierdie metodes op verdere datastelle

Unsupervised process monitoring and fault diagnoses with machine learning methods

Although this book is focused on the process industries, the methodologies discussed in the following chapters are generic and can in many instances be applied with little modification in other monitoring systems, including some of those concerned with structural health monitoring, biomedicine, environmental monitoring, the monitoring systems found in vehicles and aircraft and monitoring of computer security systems. Of course, the emphasis would differ in these other areas of interest, e.g. dynamic process monitoring and nonlinear signal processing would be more relevant to structural health analysis and brain–machine interfaces than techniques designed for steady-state systems, but the basic ideas remain intact. As a consequence, the book should also be of interest to readers outside the process engineering community, and indeed, advances in one area are often driven by application or modification of related ideas in a similar field

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

XIX, 374 p. 208 illus., 151 illus. in color.onlin

Early Detection of risk of autism spectrum disorder based on recurrence quantification analysis of electroencephalographic signals

Early detection of autism spectrum disorder (ASD) in infants is vital in maximizing the impact and potential long-term outcomes of early delivery of rehabilitative therapies. To date no definitive diagnostic test for ASD exists. Electroencephalography is a noninvasive method used to capture underlying electrical changes in brain activity. This proof-of-concept study suggests that recurrence quantification analysis features computed from resting state spontaneous eyes-closed electroencephalographic (EEG) signals may be useful biomarkers for early detection of risk of ASD

Comparative analysis of Granger causality and transfer entropy to present a decision flow for the application of oscillation diagnosis

Causality analysis techniques can be used for fault diagnosis in industrial processes. Multiple causality analysis techniques have been shown to be effective for fault diagnosis. Comparisons of some of the strengths and weaknesses of these techniques have been presented in literature. However, there are no clear guidelines on which technique to select for a specific application. These comparative studies have not thoroughly addressed all the factors affecting the selection of techniques. In this paper, these two techniques are compared based on their accuracy, precision, automatability, interpretability, computational complexity, and applicability for different process characteristics. Transfer entropy and Granger causality are popular causality analysis techniques, and therefore these two are selected for this study. The two techniques were tested on an industrial case study of a plant wide oscillation and their features were compared. To investigate the accuracy and precision of Granger causality and transfer entropy, their ability to find true connections in a simulated process was also tested. Transfer entropy was found to be more precise and the causality maps derived from it were more visually interpretable. However, Granger causality was found to be easier to automate, much less computationally expensive, and easier to interpret the meaning of the values obtained. A decision flow was developed from these comparisons to aid users in deciding when to use Granger causality or transfer entropy, as well as to aid in the interpretation of the causality maps obtained from these techniques.http://www.elsevier.com/locate/jprocont2020-07-01hj2019Electrical, Electronic and Computer Engineerin

Diagnosis of oscillations in an industrial mineral process using transfer entropy and nonlinearity index

Oscillations in mineral processes can propagate through multiple units, causing important controlled variables to deviate from their set points and degrade control performance. Root cause diagnosis of oscillations enables corrective action to return to desired operation. Numerous data-based methods for diagnosis of oscillations have been developed. Transfer entropy and the nonlinearity index are popular techniques that have been proven effective for oscillation diagnosis for a number of processes. Transfer entropy is a causality analysis technique. Causal relationships between measured variables are inferred, which allows the oscillation propagation path to be traced. The nonlinearity index ranks variables according to their nonlinearity content. The assumption is that the nonlinearity is greatest close to the oscillation source, since the process acts as a linear filter as the oscillation propagates. An oscillation propagating through multiple control loops in a mineral processing plant was identified. The validity of transfer entropy and nonlinearity index for oscillation diagnosis was compared, highlighting their benefits and pitfalls when applied to a real industrial case study. The results revealed contradictory root causes for transfer entropy and the nonlinearity index. Consideration of process knowledge indicated that the transfer entropy results were consistent with the material flow and control structure. This indicates that transfer entropy accurately traced the oscillation propagation, while the nonlinearity index gave erroneous results. Nonlinear behaviour occurring in the process caused nonlinear trends to develop downstream of the root cause, making the assumptions of the nonlinearity index invalid. This result demonstrates the need for careful analysis of fault diagnosis results using expert knowledge of the process.Anglo American Platinumhttps://www.journals.elsevier.com/ifac-papersonline2019-08-29hj2019Electrical, Electronic and Computer Engineerin