Nonlinear dynamic process monitoring using kernel methods

The application of kernel methods in process monitoring is well established. How- ever, there is need to extend existing techniques using novel implementation strate- gies in order to improve process monitoring performance. For example, process monitoring using kernel principal component analysis (KPCA) have been reported. Nevertheless, the e ect of combining kernel density estimation (KDE)-based control limits with KPCA for nonlinear process monitoring has not been adequately investi- gated and documented. Therefore, process monitoring using KPCA and KDE-based control limits is carried out in this work. A new KPCA-KDE fault identi cation technique is also proposed. Furthermore, most process systems are complex and data collected from them have more than one characteristic. Therefore, three techniques are developed in this work to capture more than one process behaviour. These include the linear latent variable-CVA (LLV-CVA), kernel CVA using QR decomposition (KCVA-QRD) and kernel latent variable-CVA (KLV-CVA). LLV-CVA captures both linear and dynamic relations in the process variables. On the other hand, KCVA-QRD and KLV-CVA account for both nonlinearity and pro- cess dynamics. The CVA with kernel density estimation (CVA-KDE) technique reported does not address the nonlinear problem directly while the regular kernel CVA approach require regularisation of the constructed kernel data to avoid com- putational instability. However, this compromises process monitoring performance. The results of the work showed that KPCA-KDE is more robust and detected faults higher and earlier than the KPCA technique based on Gaussian assumption of pro- cess data. The nonlinear dynamic methods proposed also performed better than the afore-mentioned existing techniques without employing the ridge-type regulari- sation

On the Classification of Gasoline-fuelled Engine Exhaust Fume Related Faults Using Electronic Nose and Principal Component Analysis

The efficiency and effectiveness of every equipment or system is of paramount concern to both the manufacturers and theend users, which necessitates equipment condition monitoring schemes. Intelligent fault diagnosis system using patternrecognition tools can be developed from the result of the condition monitoring. A prototype electronic nose that uses array ofbroadly tuned Taguchi metal oxide sensors was used to carry out condition monitoring of automobile engine using itsexhaust fumes with principal component analysis (PCA) as pattern recognition tool for diagnosing some exhaust relatedfaults. The results showed that the following automobile engine faults; plug-not-firing faults and loss of compression faultswere diagnosable from the automobile exhaust fumes very well with average classification accuracy of 91%.Key words: Electronic nose, Condition Monitoring, Automobile, Fault, Diagnosis, PCA

Subspace based data-driven designs of fault detection systems

The thesis focuses on advanced methods of fault detection and diagnosis suitable for application in large-scale processes. The theory of fault diagnosis mainly comprises development of mathematical models for observing critical changes in the process under consideration. The so-called residual signal is used for the purpose of detecting abnormal events and diagnosing their nature. For large-scale processes, it is difficult to build their models mathematically. Therefore, very often historical data from regular sensor measurements, event-logs and records are used to directly identify relationship between plant's input and output. On these lines, the thesis presents a data-driven design of fault detection systems which reduces the computation burden by identifying only the key components and not the entire process model itself. The novel design method is also studied within the context of parameter varying systems. Since many processes undergo temporary fluctuation of their crucial parameters, which can not be ruled out as faults, the fault detection system must be able to adapt to these changes. This is realized in the thesis with two efficient algorithms, which are based on recursive identification techniques. The theoretical contribution in this thesis also revolves around improvising the novel data-drive design of fault detection systems. In other words, the identification procedure is optimized by reformulating it as “closed-loop” identification or identification of Kalman filter. Also, the algorithm is numerically optimized by using QR based decomposition technique. The thesis also presents application results of different algorithms derived in this work. As benchmarks, the Tennessee Eastman chemical plant and the continuously stirred tank heater are considered. The novel algorithms are compared with the existing popular techniques from the literature.Die Arbeit konzentriert sich auf fortgeschrittene Methoden zur Fehlererkennung und Diagnose für den Einsatz in Mehrgrößen Systemen. Üblicherweise umfasst die Fehlerdiagnose Entwicklung von mathematischen Modellen zur Beobachtung der Veränderungen in den ursprünglichen Prozessen. Dabei wird ein so genanntes Residuensignal zur von Fehlern benutzt, welches im Fehlerfall einen Ausschlag zeigt. Für Mehrgrößen Systeme, ist es im Allgemeinen schwierig, mathematische Modelle zu erstellen, die mathematisch abgeleitet werden können. Deshalb werden Daten aus dem Prozess, z.B. aus regelmäßigen Messungen, Event-Logs oder Records verwendet, um Beziehungen zwischen Prozess-Eingang und Ausgang abzubilden. Davon ausgehend werden in der vorliegenden Arbeit Verfahren entwickelt um ein Datenbasiertes Fehlererkennungssystem zu generieren, welches ohne Modelidentifikation arbeitet. In dieser Arbeit wird das Problem der Datenbasierten Fehlererkennung weiter im Rahmen der so genannten Parameter Varianten Systeme untersucht. Da viele Prozesse vorübergehenden Parameterschwankungen unterliegen, die nicht als Fehler ausgeschlossen werden können, muss das Fehlererkennung System in der Lage sein, die Veränderungen zu adaptieren. Ein solches lernendes Fehlererkennungssystem ist hier an Hand von zwei effizienten Algorithmen und mit rekursiver Identifikation realisiert. Der Beitrag in dieser Arbeit ist auch ein modifiziertes, optimales Subraum Identifikation basiertes Entwurf. Darüber hinaus wird das Identifikationsverfahren auf die Hauptkomponenten beschränkt und das ursprüngliche Problem wird für die optimale Parameterschätzung als „Closed-Loop“ Identifikation oder Identifikation des Kalman Filters umformuliert. Die gesamte Konstruktion ist numerisch über eine QR Zerlegung numerisch optimiert. Die Arbeit stellt auch Ergebnisse der Applikation verschiedener Algorithmen vor. Als Versuchstand wurden das Tennessee Eastman Prozess und eine kontinuierlich gerührte Tankheizung verwendet. Die Algorithmen dieser Arbeit werden mit dem ursprünglichen und anderen Identifikationsverfahren verglichen

Adaptive threshold PCA for fault detection and isolation

Fault diagnosis is an important issue in industrial processes to avoid economic losses, process damage, and to guarantee safe working conditions for the operators. For high scale industrial processes the data-driven based methods are the best solution for process monitoring and fault diagnosis. Thus, in this paper, the principal component analysis is shown to detect and isolate faults. Also, a dynamic threshold is implemented to avoid false alarms because incipient faults are difficult to be detected. As a case of study, the Tennessee Eastman (TE) process is used to apply this strategy because the interaction among five units with internal control loops makes difficult to have an approached model. As results are shown the detection times, for cases where were analyzed incipient faults, the time required for fault detection must be improved, in this work, an adaptive threshold was used to reduce the false alarms but it also increases the detection times. It was concluded that the Q chart gave a better result for fault detection; the isolation times were similar to the detection ones. Two incipient faults could not be detected, the fault detection rate was similar to the shown in literature, but the detection times were better in 35% of the cases, unfortunately for four faults the detection times were bigger than the reported in other papers. It is proposed to help this method with independent component analysis due it is not guaranteed to have a Gaussian distribution in the samples

An Integrated Approach to Performance Monitoring and Fault Diagnosis of Nuclear Power Systems

In this dissertation an integrated framework of process performance monitoring and fault diagnosis was developed for nuclear power systems using robust data driven model based methods, which comprises thermal hydraulic simulation, data driven modeling, identification of model uncertainty, and robust residual generator design for fault detection and isolation. In the applications to nuclear power systems, on the one hand, historical data are often not able to characterize the relationships among process variables because operating setpoints may change and thermal fluid components such as steam generators and heat exchangers may experience degradation. On the other hand, first-principle models always have uncertainty and are often too complicated in terms of model structure to design residual generators for fault diagnosis. Therefore, a realistic fault diagnosis method needs to combine the strength of first principle models in modeling a wide range of anticipated operation conditions and the strength of data driven modeling in feature extraction. In the developed robust data driven model-based approach, the changes in operation conditions are simulated using the first principle models and the model uncertainty is extracted from plant operation data such that the fault effects on process variables can be decoupled from model uncertainty and normal operation changes. It was found that the developed robust fault diagnosis method was able to eliminate false alarms due to model uncertainty and deal with changes in operating conditions throughout the lifetime of nuclear power systems. Multiple methods of robust data driven model based fault diagnosis were developed in this dissertation. A complete procedure based on causal graph theory and data reconciliation method was developed to investigate the causal relationships and the quantitative sensitivities among variables so that sensor placement could be optimized for fault diagnosis in the design phase. Reconstruction based Principal Component Analysis (PCA) approach was applied to deal with both simple faults and complex faults for steady state diagnosis in the context of operation scheduling and maintenance management. A robust PCA model-based method was developed to distinguish the differences between fault effects and model uncertainties. In order to improve the sensitivity of fault detection, a hybrid PCA model based approach was developed to incorporate system knowledge into data driven modeling. Subspace identification was proposed to extract state space models from thermal hydraulic simulations and a robust dynamic residual generator design algorithm was developed for fault diagnosis for the purpose of fault tolerant control and extension to reactor startup and load following operation conditions. The developed robust dynamic residual generator design algorithm is unique in that explicit identification of model uncertainty is not necessary. Finally, it was demonstrated that the developed new methods for the IRIS Helical Coil Steam Generator (HCSG) system. A simulation model was first developed for this system. It was revealed through steady state simulation that the primary coolant temperature profile could be used to indicate the water inventory inside the HCSG tubes. The performance monitoring and fault diagnosis module was then developed to monitor sensor faults, flow distribution abnormality, and heat performance degradation for both steady state and dynamic operation conditions. This dissertation bridges the gap between the theoretical research on computational intelligence and the engineering design in performance monitoring and fault diagnosis for nuclear power systems. The new algorithms have the potential of being integrated into the Generation III and Generation IV nuclear reactor I&C design after they are tested on current nuclear power plants or Generation IV prototype reactors

Datenbasierter Entwurf von Fehlerdiagnosesystemen

Due to the increasing demands on system performance, production quality as well as economic operation, modern technical systems become more complicated and the automation degrees are significantly growing. To ensure the safety and reliability of such complicated processes, an effective fault diagnosis system is of prime importance in process industry nowadays. Although the model-based fault diagnosis theory has been well established, it is still difficult to establish mathematical model by means of the first principles for large-scale process. On the other hand, a large amount of historical data from regular sensor measurements, event-logs and records are often available in such industrial processes. Motivated by this observation, it is of great interest to design fault diagnosis schemes only based on the available process data. Hence, development of efficient data-driven fault diagnosis schemes for different operating conditions is the primary objective of this thesis. This thesis is firstly dedicated to the modifications on the standard multivariate statistical process monitoring approaches. The modified approaches are considerably simple, and most importantly, avoid the drawbacks of the standard techniques. As a result, the proposed approaches are able to provide enhanced fault diagnosis performance on the applications under stationary operating conditions. The further study of this thesis focuses on developing reliable fault diagnosis schemes for dynamic processes under industrial operating conditions. Instead of identifying the entire process model, primary fault diagnosis can be efficiently realized by the identification of key components. Advanced design schemes like multiple residuals generator and state observer are also investigated to ensure high fault sensitivity performance. For the large-scale processes involving changes, e.g. in operating regimes for in the manipulated variables, the recursive and adaptive techniques are studied to cope with such uncertainty issues. A novel data-driven adaptive scheme is proposed, whose stability and convergence rate are analytically proven. Compared to the standard techniques, this approach does not involve complicated on-line computation and produces consistent estimate of the unknown parameters. To illustrate the effectiveness of the derived data-driven approaches, three industrial benchmark processes, i.e. the Tennessee Eastman chemical plant, the fed-batch fermentation penicillin process and the continuously stirred tank heater, are finally considered in this thesis.Durch steigende Anforderungen an Systemverhalten, Produktqualität sowie ökonomischen Betrieb werden moderne technische Systeme stets komplizierter und ihr Grad an Automatisierung steigt wesentlich. Um die Sicherheit und Zuverlässigkeit solcher komplizierter Prozesse zu gewährleisten sind effektive Fehlerdiagnosesysteme heutzutage von großer industrieller Bedeutung. Auch wenn die Theorie zur modellbasierten Fehlerdiagnose wohl etabliert ist so ist es, gerade für komplexe Prozesse, doch schwierig das hierfür benötigte mathematische Modell basierend auf physikalischen Grundprinzipien herzuleiten. Auf der anderen Seite sind meist viele historische Daten von regulären Sensormessungen sowie Verlaufsprotokolle und -aufzeichnungen von solchen Industrieprozessen vorhanden. Motiviert hierdurch ist es von großem Interesse in der Lage zu sein ein Fehlerdiagnosesystem basierend auf eben diesen vorhandenen Daten zu entwerfen. Daher liegt der Fokus dieser Arbeit auf der Entwicklung effizienter datenbasierter Fehlerdiagnose-Schemata für verschiedene Einsatzzwecke. Diese Arbeit ist hauptsächlich den Modifikationen der Standardansätze der multivariaten statistischen Prozessüberwachung gewidmet. Die modifizierten Ansätze sind deutlich einfacher als die Standardverfahren und umgehen dazu noch deren Nachteile. Als ein Ergebnis sind die vorgeschlagenen Ansätze in der Lage eine höhere Güte der Fehlerdiagnose bei Anwendungen mit stationären Betriebsbedingungen zu gewährleisten. Die weiteren Untersuchungen dieser Arbeit befassen sich mit der Entwicklung von zuverlässigen Fehlerdiagnoseschemata für dynamische Prozesse unter industriellen Betriebsbedingungen. Statt das gesamte Prozessmodell identifizieren zu müssen kann hierbei die Erkennung der Hauptfehler meist effizient realisiert werden indem nur Schlüsselkomponenten identifiziert werden. Fortgeschrittene Entwurfsschemata wie zum Beispiel multiple Residuengeneratoren und Zustandsbeobachter werden ebenso untersucht um eine hohe Fehlersensitivitäts-Güte sicherzustellen. Für Großprozesse die Änderungen, in zum Beispiel ihren Betriebspunkten oder den manipulierten Variablen, unterworfen sind werden rekursive und adaptive Techniken untersucht um Unsicherheiten begegnen zu können. Hierzu wird ein neues datenbasiertes, adaptives Schema vorgeschlagen dessen Stabilität und Konvergenzrate analytisch bewiesen werden. Verglichen mit Standardtechniken beinhaltet dieser Ansatz keine komplizierten Onlineberechnungen und erzeugt eine konsistente Schätzung der unbekannten Parameter. Um die Effektivität der hergeleiteten datenbasierten Ansätze zu zeigen werden diese am Ende der Arbeit an drei verschiedenen industriellen Beispielprozessen, dem Tennessee Eastman Chemieprozess, dem Penizilin Batch-Edukt Fermentations-Prozess und dem Rührkesselreaktor simulativ erprobt

Batch Control and Diagnosis

Batch processes are becoming more and more important in the chemical process industry, where they are used in the manufacture of specialty materials, which often are highly profitable. Some examples where batch processes are important are the manufacturing of pharmaceuticals, polymers, and semiconductors. The focus of this thesis is exception handling and fault detection in batch control. In the first part an internal model approach for exception handling is proposed where each equipment object in the control system is extended with a state-machine based model that is used on-line to structure and implement the safety interlock logic. The thesis treats exception handling both at the unit supervision level and at the recipe level. The goal is to provide a structure, which makes the implementation of exception handling in batch processes easier. The exception handling approach has been implemented in JGrafchart and tested on the batch pilot plant Procel at Universitat Politècnica de Catalunya in Barcelona, Spain. The second part of the thesis is focused on fault detection in batch processes. A process fault can be any kind of malfunction in a dynamic system or plant, which leads to unacceptable performance such as personnel injuries or bad product quality. Fault detection in dynamic processes is a large area of research where several different categories of methods exist, e.g., model-based and process history-based methods. The finite duration and non-linear behavior of batch processes where the variables change significantly over time and the quality variables are only measured at the end of the batch lead to that the monitoring of batch processes is quite different from the monitoring of continuous processes. A benchmark batch process simulation model is used for comparison of several fault detection methods. A survey of multivariate statistical methods for batch process monitoring is performed and new algorithms for two of the methods are developed. It is also shown that by combining model-based estimation and multivariate methods fault detection can be improved even though the process is not fully observable

Process fault detection and diagnosis of fed-batch plant using multiway principal component analysis

With the advent of new technologies, process plants whether it be continuous or batch process plants are getting complex. And modelling them mathematical is a herculean task. Model based fault detection and diagnosis mainly depend on explicit mathematical model of process plant, which is the biggest problem with the model based approach. Whereas with process history based there is no need of explicit model of the plant. It only depends on the data of previous runs. With the advancement in electronic instrumentation, we can get large amount of data electronically. But the crude data we get is not useful for taking any decision. So we need develop techniques which can convey us the information about the ongoing process. So we take the help of multivariate statistics such as Principal Component Analysis(PCA) or Partial Least Squares(PLS). These methods exploits the facts such as the process data are highly correlated and have large dimensions, due to which we can compress them to lower dimension space. By examining the data in the lower dimensional space we can monitor the plant and can detect fault

Integrating PCA and structural model decomposition to improve fault monitoring and diagnosis with varying operation points

Producción CientíficaFast and efficient fault monitoring and diagnostics methods are essential for fault diagnosis and prognosis tasks in Health Monitoring Systems. These tasks are even more complicated when facing dynamic systems with multiple operation points. This article introduces a symbiotic solution for fault detection and isolation, based on the integration of two complementary techniques: Possible Conflicts (PCs), a model-based diagnosis technique from the Artificial Intelligence (AI) community, and Principal Component Analysis (PCA), a Multivariate Statistical Process Control (MSPC) technique. Our proposal improves the PCA-based fault detection in systems with multiple operation points and transient states and provides a straightforward fault isolation stage for PCA. At the same time, the proposal increases the robustness for fault detection using PCs through the application of PCA to the residual signals. PCA has the ability to filter out residual deviations caused by model uncertainties that can lead to a high number of false positives. The proposed method has been successfully tested in a real-world plant with accurate fault detection results. The plant has noisy sensors and a system model without the same accuracy at each operation point and transient states.Ministerio de Ciencia e Innovación (PID2021-126659OB-I00