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A detecção de falhas em uma plataforma distribuída é um componente essencial para uma grande quantidade de estratégias de tolerância a falhas, como a restauração do estado das aplicações distribuídas através de checkpointing e message logging. Porém, esta detecção frequentemente depende da comunicação confiável entre os nós de processamento e os módulos de detecção de falhas. Em grades computacionais hierárquicas com limitações de conectividade, a comunicação direta entre nós e módulos de detecção é muitas vezes impossível. Outro fator que dificulta a detecção de falhas em grades computacionais é a localização geograficamente esparsa entre as instituições e os recursos computacionais disponíveis na grade e a consequente utilização de redes de longa distância para os conectar. Esta dissertação apresenta uma arquitetura para a detecção de falhas em plataformas distribuídas otimizada para o funcionamento em grades computacionais hierárquicas, levando suas limitações e requisitos em consideração. A arquitetura, denominada GFDA (Grid Fault Detection Architecture), é estruturada em módulos de detecção das falhas que afetam nós computacionais disponibilizados na grade, módulos de detecção de falhas das aplicações distribuídas, e módulos de coleção, processamento e encaminhamento das notificações de falha e recuperação emitidas pelos módulos de detecção. Detalhes da implementação e da verificação do funcionamento correto da arquitetura são apresentados, bem como resultados obtidos através da execução de componentes da arquitetura em um cluster de computadores simulado através de máquinas virtuais. São propostas técnicas para a otimização da qualidade de serviço de detecção de falhas. Os resultados obtidos com a utilização destas técnicas são comparados com resultados obtidos com abordagens tradicionais. Observa-se que as técnicas implementadas na arquitetura GFDA para o processamento de notificações de falha e recuperação e a introdução de redundância nas mensagens trocadas entre os módulos de detecção de falhas traz resultados positivos em condições adversas de conectividade. Conclui-se que a arquitetura GFDA contribui para o estabelecimento de uma solução viável para a detecção de falhas em uma grade computacional hierárquica em que há restrições de conectividade entre os nós computacionais. / In distributed platforms, fault detection is an essential requirement to a wide range of fault tolerance techniques, such as restoring the state of distributed applications with checkpointing and message logging. However, fault detection often depends on reliable communication between the processing nodes and detection fault modules. Direct communication between the nodes and detection modules is often impossible in hierarchical grid computing platforms. The physical distance between the institutions and resources available on the grid, and thus the requirement of long distance networks connecting them, is another factor that makes direct fault detection in computer grids a challenge. This thesis presents a fault detection architecture for distributed platforms, optimized for usage in hierarchical grids and thus taking into account its restrictions and requirements. The architecture, named GFDA (Grid Fault Detection Architecture), is structured as fault detection modules for faults that affect the computing nodes available on the grid, detection modules for faults that affect the distributed applications, and modules that perform the collection, processing and forwarding of the fault and recovery notifications generated by the detection modules. This thesis presents implementation details, an evaluation of the correctness of the designed architecture, and results obtained through the deployment of parts of the architecture in a simulated cluster that uses virtual machines to simulate computing nodes. Techniques to optimize the quality of the detection fault service are proposed. The results obtained through the usage of such techniques are compared to the results obtained through traditional approaches. Positive results were extracted even under adverse connectivity conditions by using techniques such as the processing of fault and recovery notifications and the introduction of redundant information in the messages exchanged between the detection modules. It is concluded that the GFDA architecture contributes to the establishment of a viable solution for fault detection in a hierarchical grid computing platform that presents connectivity restrictions between the nodes.

A detecção de falhas em uma plataforma distribuída é um componente essencial para uma grande quantidade de estratégias de tolerância a falhas, como a restauração do estado das aplicações distribuídas através de checkpointing e message logging. Porém, esta detecção frequentemente depende da comunicação confiável entre os nós de processamento e os módulos de detecção de falhas. Em grades computacionais hierárquicas com limitações de conectividade, a comunicação direta entre nós e módulos de detecção é muitas vezes impossível. Outro fator que dificulta a detecção de falhas em grades computacionais é a localização geograficamente esparsa entre as instituições e os recursos computacionais disponíveis na grade e a consequente utilização de redes de longa distância para os conectar. Esta dissertação apresenta uma arquitetura para a detecção de falhas em plataformas distribuídas otimizada para o funcionamento em grades computacionais hierárquicas, levando suas limitações e requisitos em consideração. A arquitetura, denominada GFDA (Grid Fault Detection Architecture), é estruturada em módulos de detecção das falhas que afetam nós computacionais disponibilizados na grade, módulos de detecção de falhas das aplicações distribuídas, e módulos de coleção, processamento e encaminhamento das notificações de falha e recuperação emitidas pelos módulos de detecção. Detalhes da implementação e da verificação do funcionamento correto da arquitetura são apresentados, bem como resultados obtidos através da execução de componentes da arquitetura em um cluster de computadores simulado através de máquinas virtuais. São propostas técnicas para a otimização da qualidade de serviço de detecção de falhas. Os resultados obtidos com a utilização destas técnicas são comparados com resultados obtidos com abordagens tradicionais. Observa-se que as técnicas implementadas na arquitetura GFDA para o processamento de notificações de falha e recuperação e a introdução de redundância nas mensagens trocadas entre os módulos de detecção de falhas traz resultados positivos em condições adversas de conectividade. Conclui-se que a arquitetura GFDA contribui para o estabelecimento de uma solução viável para a detecção de falhas em uma grade computacional hierárquica em que há restrições de conectividade entre os nós computacionais. / In distributed platforms, fault detection is an essential requirement to a wide range of fault tolerance techniques, such as restoring the state of distributed applications with checkpointing and message logging. However, fault detection often depends on reliable communication between the processing nodes and detection fault modules. Direct communication between the nodes and detection modules is often impossible in hierarchical grid computing platforms. The physical distance between the institutions and resources available on the grid, and thus the requirement of long distance networks connecting them, is another factor that makes direct fault detection in computer grids a challenge. This thesis presents a fault detection architecture for distributed platforms, optimized for usage in hierarchical grids and thus taking into account its restrictions and requirements. The architecture, named GFDA (Grid Fault Detection Architecture), is structured as fault detection modules for faults that affect the computing nodes available on the grid, detection modules for faults that affect the distributed applications, and modules that perform the collection, processing and forwarding of the fault and recovery notifications generated by the detection modules. This thesis presents implementation details, an evaluation of the correctness of the designed architecture, and results obtained through the deployment of parts of the architecture in a simulated cluster that uses virtual machines to simulate computing nodes. Techniques to optimize the quality of the detection fault service are proposed. The results obtained through the usage of such techniques are compared to the results obtained through traditional approaches. Positive results were extracted even under adverse connectivity conditions by using techniques such as the processing of fault and recovery notifications and the introduction of redundant information in the messages exchanged between the detection modules. It is concluded that the GFDA architecture contributes to the establishment of a viable solution for fault detection in a hierarchical grid computing platform that presents connectivity restrictions between the nodes.

Residue codes have successfully been used for decades as a low overhead method of arithmetic error detection. This work explores the design space of residue checking for error detection in processors with modern word sizes and technology nodes. The area overheads of detecting arithmetic errors are considered for a variety of processor configurations, ranging from those best suited for embedded processors to those best for high-performance computers. The ultimate goal of this work is to enable the study of low overhead arithmetic error protection and correction in a wider variety of computer architectures than has previously been attempted in a systematic manner. / text

Software developers spend much time on finding and fixing software faults, both during the development and the maintanence of the system. Despite this, there hasn’t been much research done on the effort that these activities require. It is important, both for developers and clients of software systems, to know how they can use their resources as efficiently as possible. Since software errors can cause high costs and can result in serious consequences, it is therefore of interest to have a basis for how much time is spent on finding and fixing software errors. Software Fault Injection (SFI) is a method of injecting artificial bugs into software, which is used to assess a program’s reliability. This study looks at the possibility of using SFI to develop a framework in order to measure the effort in finding and fixing errors. An existing software system, EXIT, was injected with a set of predefined errors. They were then detected and corrected with activities obtained from previous research conducted by other researchers. The result was the Software Fault Injection/Detection Framework (SFIDF). A framework that can be used to measure the time distribution for injection, detection and the fixing of software errors. / Mjukvaruutvecklare spenderar mycket tid på att hitta och åtgärda defekter i mjukvara, både under utvecklandet och underhållandet av system. Trots detta har det inte forskats särskilt mycket på insatsen som dessa aktiviteter kräver. Det är viktigt både för utvecklare och beställare av mjukvarusystem att veta hur de kan disponera sina resurser så effektivt som möjligt. Eftersom mjukvarufel kan orsaka stora kostnader och resultera i allvarliga konsekvenser, är det av intresse att ha underlag för hur mycket tid som går åt till att hitta och åtgärda mjukvarufel. Software Fault Injection (SFI) är en metod för att injicera artificiella fel i mjukvara, som används för att bedöma ett programs pålitlighet. I denna studie har vi med hjälp av SFI fokuserat på att ta fram ett ramverk för att mäta insatsen som krävs för att hitta och åtgärda fel. Vi använde oss av ett befintligt mjukvarusystem, EXIT, som injicerades med en uppsättning fördefinierade fel. Dessa upptäcktes och åtgärdades med aktiviteter hämtat från tidigare forskning. Resultatet blev Software Fault Injection Detection Framework (SFIDF). Ett ramverk som kan användas för att mäta insatsdistributionen för både injicering, upptäckt och åtgärdande av fel i mjukvara.

Thesis (MEng) -- Stellenbosch University, 2014. / ENGLISH ABSTRACT: Modern mineral processing plants are characterised by a large number of measured variables, interacting through numerous processing units, control loops and often recycle streams. Consequentially, faults in these plants propagate throughout the system, causing significant degradation in performance. Fault diagnosis therefore forms an essential part of performance monitoring in such processes. The use of feature extraction methods for fault diagnosis has been proven in literature to be useful in application to chemical or minerals processes. However, the ability of these methods to identify the causes of the faults is limited to identifying variables that display symptoms of the fault. Since faults propagate throughout the system, these results can be misleading and further fault identification has to be applied. Faults propagate through the system along material, energy or information flow paths, therefore process topology information can be used to aid fault identification. Topology information can be used to separate the process into multiple blocks to be analysed separately for fault diagnosis; the change in topology caused by fault conditions can be exploited to identify symptom variables; a topology map of the process can be used to trace faults back from their symptoms to possible root causes. The aim of this project, therefore, was to develop a process monitoring strategy that exploits process topology for fault detection and identification. Three methods for extracting topology from historical process data were compared: linear cross-correlation (LC), partial cross-correlation (PC) and transfer entropy (TE). The connectivity graphs obtained from these methods were used to divide process into multiple blocks. Two feature extraction methods were then applied for fault detection: principal components analysis (PCA), a linear method, was compared with kernel PCA (KPCA), a nonlinear method. In addition, three types of monitoring chart methods were compared: Shewhart charts; exponentially weighted moving average (EWMA) charts; and cumulative sum (CUSUM) monitoring charts. Two methods for identifying symptom variables for fault identification were then compared: using contributions of individual variables to the PCA SPE; and considering the change in connectivity. The topology graphs were then used to trace faults to their root causes. It was found that topology information was useful for fault identification in most of the fault scenarios considered. However, the performance was inconsistent, being dependent on the accuracy of the topology extraction. It was also concluded that blocking using topology information substantially improved fault detection and fault identification performance. A recommended fault diagnosis strategy was presented based on the results obtained from application of all the fault diagnosis methods considered. / AFRIKAANSE OPSOMMING: Moderne mineraalprosesseringsaanlegte word gekarakteriseer deur ʼn groot aantal gemete veranderlikes, wat in wisselwerking tree met mekaar deur verskeie proseseenhede, beheerlusse en hersirkulasiestrome. As gevolg hiervan kan foute in aanlegte deur die hele sisteem propageer, wat prosesprestasie kan laat afneem. Foutdiagnose vorm dus ʼn noodsaaklike deel van prestasiemonitering. Volgens literatuur is die gebruik van kenmerkekstraksie metodes vir foutdiagnose nuttig in chemiese en mineraalprosesseringsaanlegte. Die vermoë van hierdie metodes om die fout te kan identifiseer is egter beperk tot die identifikasie van veranderlikes wat simptome van die fout vertoon. Aangesien foute deur die sisteem propageer kan resultate misleidend wees, en moet verdere foutidentifikasie metodes dus toegepas word. Foute propageer deur die proses deur materiaal-, energie- of inligtingvloeipaaie, daarom kan prosestopologie inligting gebruik word om foutidentifikasie te steun. Topologie inligting kan gebruik word om die proses in veelvoudige blokke te skei om die blokke apart te ontleed. Die verandering in topologie veroorsaak deur fouttoestande kan dan analiseer word om simptoomveranderlikes te identifiseer. ʼn Topologiekaart van die proses kan ontleed word om moontlike hoofoorsake van foute op te spoor. Die doel van hierdie projek was dus om ʼn prosesmoniteringstrategie te ontwikkel wat prosestopologie benut vir fout-opspooring en foutidentifikasie. Drie metodes vir topologie-ekstraksie van historiese prosesdata is met mekaar vergelyk: liniêre kruiskorrelasie, parsiële kruiskorrelasie en oordrag-entropie. Konnektiwiteitsgrafieke verkry deur hierdie ekstraksie-metodes is gebruik om die proses in veelvoudige blokke te skei. Twee kenmerkekstraksiemetodes is hierna toegepas om foutdeteksie te bewerkstellig: hoofkomponentanalise (HKA), ʼn liniêre metode; en kernhoofkomponentanalise (KHKA), ʼn nie-lineêre metode. Boonop was drie tipes moniteringskaart metodes vergelyk: Shewhart kaarte, eksponensieel-geweegde bewegende gemiddelde kaarte en kumulatiewe som kaarte. Twee metodes om simptoom veranderlikes te identifiseer vir foutidentifikasie was daarna vergelyk: gebruik van individuele veranderlikes; en inagneming van die verandering in konnektiwiteit. Die konnektiwiteitgrafieke was daarna gebruik om hoofoorsake van foute op te spoor. Dit is gevind dat topologie informasie nuttig was vir foutidentifikasie vir meeste van die fouttoestande ondersoek. Nogtans was die prestasie onsamehangend, aangesien dit afhanklik is van die akkuraatheid waarmee topologie ekstraksie uitgevoer is. Daar was ook afgelei dat die gebruik van topologie blokke beduidend die fout-opspooring en foutidentifikasie prestasie verbeter het. ʼn Aanbevole foutdiagnose strategie is voorgestel.

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.

In this dissertation, we explore the problem of fault detection and fault diagnosis for systems modeled as condition systems. A condition system is a Petri net based framework of components which interact with each other and the external environment through the use of condition signals. First, a system FAULT is defined as an observed behavior which does not correspond to any expected behavior, where the expected behavior is defined through condition system models. A DETECTION is the determination that the system is not behaving as expected according to the model of the system. A DIAGNOSIS of this fault localizes the subsystem that is the source of the discrepancy between output and expected observations. We characterize faults as a behavior relaxation of model components. We then show that detection and diagnosis can be determined in a finite number of calculations. The exact solution can be computationally involved, so we also present methods to perform a rapid detection and diagnosis. We have also included a chapter on a conversion from the condition system framework into a linear-time temporal logic(LTL) framework.

This work is dedicated to faulty induction motors. These motors are often used in industrial applications thanks to their usability and their robustness. However, nowadays optimisation of production becomes so critical that the conceptual reliability of the motor is not sufficient anymore. Motor condition monitoring is expanding to serve maintenance planning and uptime maximisation. Moreover, the use of drive control sensors (namely stator current and voltage) can avoid the installation and maintenance of dedicated sensors for condition monitoring. Many authors are working in this field but few approach the diagnosis from a detailed and clear physical understanding of the localised phenomena linked to the faults. Broken bars are known to modulate stator currents but it is shown in this work that it also changes machine saturation level in the neighbourhood of the bar. Furthermore, depending on the voltage level, this change in local saturation affects the amplitude and the phase of the modulation. This is of major importance as most diagnosis techniques use this feature to detect and quantify broken bars. For stator short-circuits, a high current is flowing in the short-circuited coil due to mutual coupling with the other windings and current spikes are flowing in the rotor bars as they pass in front of the short-circuited conductors. In the case of rotor eccentricities, the number of pole-pairs and the connection of these pole-pairs greatly affect the airgap flux density distribution as well as the repartition of the line currents in the different pole-pairs. These conclusions are obtained through the use of time-stepping finite element models of the faulty motors. Moreover, circuit models of faulty machines are built based on the conclusions of previously explained fault analysis and on classical Park models. A common mathematical description is used which allows objective comparison of the models for representation of the machine behaviour and computing time. The identifiability of the parameters of the models as well as methods for their identification are studied. Focus is set on the representation of the machine behaviour using these parameters more than the precise identification of the parameters. It is shown that some classical parameters can not be uniquely identified using only stator measurements. Fault detection and identification using computationally cheap models are compared to advanced detection through motor stator current spectral analysis. This last approach allows faster detection and identification of the fault but leads to incorrect conclusions in low load conditions, in transient situations or in perturbed environments (i.e. fluctuating load torque and unideal supply). Efficient quantification of the fault can be obtained using detection techniques based on the comparison of the process to a model. Finally, the work provides guidelines for motor supervision strategies depending on the context of motor utilisation.

The safety and functionality of engineering systems can be affected adversely by faults or wear in system components. Therefore, methods for detecting such faults/wear and ameliorating their effects to avoid system failure are important. Designing schemes for the detection and diagnosis of faults is becoming increasingly important in engineering due to the complexity of modern industrial systems and growing demands for quality, cost efficiency, reliability, and the safety issue. In safety/mission critical applications, fault detection can be combined with accommodation/reconfiguration (after a fault) to achieve fault tolerance allowing the system to complete or abort its function in a way that is sub-optimal but does achieve the design objective. This thesis discusses research carried-out on the development and validation of a model-based fault detection and isolation (FDI) system for a pneumatically actuated Stewart platform. The Stewart-Gough platform provides six degrees of freedom consisting of three translational and three rotational degrees of freedom (x, y, z, pitch, roll, & yaw). As these platforms can be fast acting (rapid motion) and can handle reasonable loads, they can become dangerous, especially when fault(s) in the platform mechanism, drivetrain or control system occur. Therefore, as a safety critical application it is imperative that fault tolerant schemes are applied in order to provide a safe working environment. The design concept of the FDI scheme for the full Stewart-Gough platform is first designed using a single cylinder set-up. This modular concept is adopted so that a robust fault tolerant control scheme can be designed basically off-line (i.e. not attached to the Stewart Gough platform). This approach is adopted as requirements are easier to understand using a single cylinder set-up. The modular design approach subdivides the whole system into smaller sections (modules) that can be independently created and then used in the complete Stewart-Gough platform. The main contributions of the work are that a pneumatically actuated Stewart-Gough platform has been designed, built, and commissioned. A mathematical model has been developed and has been validated against experimental results. Two control approaches have been designed and compared. A fundamental comparative study of parity equations and Kalman filter observer banks for fault detection in pneumatic actuators has been conducted. The parity equations and Kalman filter approaches have been extended to provide a combined fault detection scheme. The FDI and control schemes have been combined in a modular Fault Tolerant Control (FTC) scheme for a pneumatic cylinder. The resulting FTC scheme has been validated by experimentation and demonstrated on the single cylinder test rig. The FTC scheme has been extended to all 6 cylinders (and including fault management at top level) of Stewart-Gough platform. The FTC scheme has been validated by experimentation and demonstrated on the Stewart-Gough platform test rig.