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This dissertation addresses fault detection and isolation (FDI) for nonlinear systems based on models using two different approaches. The first approach detects and isolates single and multiple faults, particularly when there are restrictions in measuring process variables. The FDI model-based method is based on nonlinear state estimators, in which the estimates are calculated under high filtering, and a high fidelity residuals model, obtained from the difference between measurements and estimates. In the second approach, a robust fault detection and isolation (RFDI) system, that handles both parameter estimation and parameters with uncertainties, is proposed in which complex models can be simplified with nonlinear functions so that they can be formulated as differential algebraic equations (DAE). In utilizing this framework, faults are identified by performing a statistical analysis. Finally, comparisons with existing data-driven approaches show that the proposed model-based methods are capable of distinguishing a fault from the diverse array of possible faults, a common occurrence in complex processes. / text

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Wireless sensor networks (WSNs) can provide new methods for information gathering for a variety of applications. In order to ensure the network quality of service, the quality of the measurements has to be guaranteed. Distributed fault detection and isolation schemes are preferred to centralized solutions to diagnose faulty sensors in WSNs. Indeed the first approach avoids the need for a central node that collects information from every sensor node, and hence it limits complexity and energy cost while improving reliability.In the case of state estimation over distributed architectures, the sensor faults can be propagated in the network during the information exchanging process. To build a reliable state estimate one has to make sure that the measurements issued by the different sensors are fault free. That is one of the motivations to build a distributed fault detection and isolation (FDI) system that generates an alarm as soon as a measurement is subject to a fault (has drift, cdots ). In order to diagnose faults with small magnitude in wireless sensor networks, a systematic methodology to design and implement a distributed FDI system is proposed. It resorts to distinguishability measures to indicate the performance of the FDI system and to select the most suitable node(s) for information exchange in the network with a view to FDI. It allows one to determine the minimum amount of data to be exchanged between the different nodes for a given FDI performance. In this way, the specifications for FDI can be achieved while the communication and computation cost are kept as small as possible. The distributed FDI systems are designed both in deterministic and stochastic frameworks. They are based on the parity space approach that exploits spacial redundancy as well as temporal redundancy in the context of distributed schemes. The decision systems with the deterministic method and the stochastic method are designed not only to detect a fault but also to distinguish which fault is occurring in the network. A case study with a WSN is conducted to verify the proposed method. The network is used to monitor the temperature and humidity in a computer room. The distributed FDI system is validated both with simulated data and recorded data. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

In this thesis, a methodology for the detection of anomalies in the cardiovascular system is presented. The cardiovascular system is one of the most fascinating and complex physiological systems. Nowadays, cardiovascular diseases constitute one of the most important causes of mortality in the world. For instance, an estimate of 17.3 million people died in 2008 from cardiovascular diseases. Therefore, many studies have been devoted to modeling the cardiovascular system in order to better understand its behavior and find new reliable diagnosis techniques. The lumped parameter model of the cardiovascular system proposed in [1] is restructured using a hybrid systems approach in order to include a discrete input vector that represents the influence of the mitral and aortic valves in the different phases of the cardiac cycle. Parting from this model, a Taylor expansion around the nominal values of a vector of parameters is conducted. This expansion serves as the foundation for a component fault detection process to detect changes in the physiological parameters of the cardiovascular system which could be associated with cardiovascular anomalies such as atherosclerosis, aneurysm, high blood pressure, etc. An Extended Kalman Filter is used in order to achieve a joint estimation of the state vector and the changes in the considered parameters. Finally, a bank of filters is, as in [2], used in order to detect the appearance of heart valve diseases, particularly stenosis and regurgitation. The first numerical results obtained are presented.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: In this project, system identification is done on the Modular Unmanned Aerial Vehicle (UAV). This is necessary to perform fault detection and isolation, which is part of the Fault Tolerant Control research project at Stellenbosch University. The equations necessary to do system identification are developed. Various methods for system identification is discussed and the regression methods are implemented. It is shown how to accommodate a sudden change in aircraft parameters due to a fault. Smoothed numerical differentiation is performed in order to acquire data necessary to implement the regression methods. Practical issues regarding system identification are discussed and methods for addressing these issues are introduced. These issues include data collinearity and identification in a closed loop. The regression methods are implemented on a simple roll model of the Modular UAV in order to highlight the various difficulties with system identification. Different methods for accommodating a fault are illustrated. System identification is also done on a full nonlinear model of the Modular UAV. All the parameters converges quickly to accurate values, with the exception of Cl R , CnP and Cn A . The reason for this is discussed. The importance of these parameters in order to do Fault Tolerant Control is also discussed. An S-function that implements the recursive least squares algorithm for parameter estimation is developed. This block accommodates for the methods of applying the forgetting factor and covariance resetting. This block can be used as a stepping stone for future work in system identification and fault detection and isolation. / AFRIKAANSE OPSOMMING: In hierdie projek word stelsel identifikasie gedoen op die Modulêre Onbemande Vliegtuig. Dit is nodig om foutopsporing en isolasie te doen wat ’n deel uitmaak van fout verdraagsame beheer. Die vergelykings wat nodig is om stelsel identifikasie te doen is ontwikkel. Verskeie metodes om stelsel identifikasie te doen word bespreek en die regressie metodes is uitgevoer. Daar word gewys hoe om voorsiening te maak vir ’n skielike verandering in die vliegtuig parameters as gevolg van ’n fout. Reëlmatige numeriese differensiasie is gedoen om data te verkry wat nodig is vir die uitvoering van die regressie metodes. Praktiese kwessies aangaande stelsel identifikasie word bespreek en metodes om hierdie kwessies aan te spreek word gegee. Hierdie kwessies sluit interafhanklikheid van data en identifikasie in ’n geslote lus in. Die regressie metodes word toegepas op ’n eenvoudige rol model van die Modulêre Onbemande Vliegtuig om die verskeie kwessies aangaande stelsel identifikasie uit te wys. Verskeie metodes vir die hantering vir ’n fout word ook illustreer. Stelsel identifikasie word ook op die volle nie-lineêre model van die Modulêre Onbemande Vliegtuig gedoen. Al die parameters konvergeer vinnig na akkurate waardes, met die uitsondering van Cl R , CnP and Cn A . Die belangrikheid van hierdie parameters vir fout verdraagsame beheer word ook bespreek. ’n S-funksie blok vir die rekursiewe kleinste-kwadraat algoritme is ontwikkel. Hierdie blok voorsien vir die metodes om die vergeetfaktor en kovariansie herstelling te implementeer. Hierdie blok kan gebruik word vir toekomstige werk in stelsel identifikasie en foutopsporing en isolasie.

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems.

Fault diagnosis in discrete event systems is studied using a state-based framework. Faults can be either intermittent or permanent. For intermittent faults, system may recover from faulty behaviour through reset. To diagnose such intermittent faults, fault counters are introduced. Fault counters record the number of intermittent faults which must have occurred according to the output observations. This provides the main diagnosis. They also record the number of possible intermittent faults which may have occurred but cannot be confirmed. This provides auxiliary diagnostic information. Fault diagnosability is then studied. Since faults may be intermittent, they may occur repeatedly. Three different notions are studied: 1-diagnosability, 1,k-diagnosability, and 1,infty-diagnosability, and criteria for each of these notions are obtained. The criteria are expressed in terms of fault counters and extend the diagnosability criteria for permanent faults. The concept of a resonant path is introduced, which plays an important role in studying diagnosability.

Fault diagnosis in discrete event systems is studied using a state-based framework. Faults can be either intermittent or permanent. For intermittent faults, system may recover from faulty behaviour through reset. To diagnose such intermittent faults, fault counters are introduced. Fault counters record the number of intermittent faults which must have occurred according to the output observations. This provides the main diagnosis. They also record the number of possible intermittent faults which may have occurred but cannot be confirmed. This provides auxiliary diagnostic information. Fault diagnosability is then studied. Since faults may be intermittent, they may occur repeatedly. Three different notions are studied: 1-diagnosability, 1,k-diagnosability, and 1,infty-diagnosability, and criteria for each of these notions are obtained. The criteria are expressed in terms of fault counters and extend the diagnosability criteria for permanent faults. The concept of a resonant path is introduced, which plays an important role in studying diagnosability.

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