1,424 research outputs found

    Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process

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    Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach

    Statistical process monitoring of a multiphase flow facility

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    Industrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/download.html〉 Accessed 21.03.2014). The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms

    Datenbasierter Entwurf von Fehlerdiagnosesystemen

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    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

    An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

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    An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further

    Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

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    The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

    Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

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    This study provides a review of methods used for stability monitoring in two different fields, electrical power systems and vibration analysis, with the aim of increasing awareness of and highlighting opportunities for cross-fertilisation. The nature of the problems that require stability monitoring in both fields are discussed here as well as the approaches that have been taken. The review of power systems methods is presented in two parts: methods for ambient or normal operation and methods for transient or post-fault operation. Similarly, the review of methods for vibration analysis is presented in two parts: methods for stationary or linear time-invariant data and methods for non-stationary or non-linear time-variant data. Some observations and comments are made regarding methods that have already been applied in both fields including recommendations for the use of different sets of algorithms that have not been utilised to date. Additionally, methods that have been applied to vibration analysis and have potential for power systems stability monitoring are discussed and recommended. � 2010 The Institution of Engineering and Technology

    Data-driven Soft Sensors in the Process Industry

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    In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

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

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    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
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