Hoists and cranes exist in many contexts around the world, often carrying veryheavy loads. The safety for the user and bystanders is of utmost importance. Thisthesis investigates whether it is possible to perform fault detection on a systemlevel, measuring the inputs and outputs of the system without introducing newsensors. The possibility of detecting dangerous faults while letting safe faultspass is also examined.A mathematical greybox model is developed and the unknown parametersare estimated using data from a labscale test crane. Validation is then performedwith other datasets to check the accuracy of the model. A linear observer of thesystem states is created using the model. Simulated fault injections are made,and different fault detection methods are applied to the residuals created withthe observer. The results show that dangerous faults in the system or the sensorsthemselves are detectable, while safe faults are disregarded in many cases.The idea of performing model-based fault detection from a system point ofview shows potential, and continued investigation is recommended.

Fault Detection and Diagnosis (FDD) constitutes an essential aspect of modern life, with far-reaching implications spanning various domains such as healthcare, maintenance of industrial machinery, and cybersecurity. A comprehensive approach to FDD entails addressing facets related to detection, invariance, isolation, identification, and supervision. In FDD, there are two main perspectives: model-based and data-driven approaches. This thesis centers on model-based methodologies, particularly within the context of control and industrial applications. It introduces novel estimation strategies aimed at enhancing computational efficiency, addressing fault discretization, and considering robustness in fault detection strategies. In cases where the system's behavior can vary over time, particularly in contexts like fault detection, presenting multiple scenarios is essential for accurately describing the system. This forms the underlying principle in Multiple Model Adaptive Estimation (MMAE) like well-established Interacting Multiple Model (IMM) strategy. In this research, an exploration of an efficient version of the IMM framework, named Updated IMM (UIMM), is conducted. UIMM is applied for the identification of irreversible faults, such as leakage and friction faults, within an Electro-Hydraulic Actuator (EHA). It reduces computational complexity and enhances fault detection and isolation, which is very important in real-time applications such as Fault-Tolerant Control Systems (FTCS). Employing robust estimation strategies such as the Smooth Variable Structure Filter (SVSF) in the filter bank of this algorithm will significantly enhance its performance, particularly in the presence of system uncertainties. To relax the irreversible assumption used in the UIMM algorithm and thereby expanding its application to a broader range of problems, the thesis introduces the Moving Window Interacting Multiple Model (MWIMM) algorithm. MWIMM enhances efficiency by focusing on a subset of possible models, making it particularly valuable for fault intensity and Remaining Useful Life (RUL) estimation. Additionally, this thesis delves into exploring chattering signals generated by the SVSF filter as potential indicators of system faults. Chattering, arising from model mismatch or faults, is analyzed for spectral content, enabling the identification of anomalies. The efficacy of this framework is verified through case studies, including the detection and measurement of leakage and friction faults in an Electro-Hydraulic Actuator (EHA). / Thesis / Candidate in Philosophy / In everyday life, from doctors diagnosing illnesses to mechanics inspecting cars, we encounter the need for fault detection and diagnosis (FDD). Advances in technology, like powerful computers and sensors, are making it possible to automate fault diagnosis processes and take corrective actions in real-time when something goes wrong. The first step in fault detection and diagnosis is to precisely identify system faults, ensuring they can be properly separated from normal variations caused by uncertainties, disruptions, and measurement errors. This thesis explores model-based approaches, which utilize prior knowledge about how a normal system behaves, to detect abnormalities or faults in the system. New algorithms are introduced to enhance the efficiency and flexibility of this process. Additionally, a new strategy is proposed for extracting information from a robust filter, when used for identifying faults in the system.

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

Reversible logic computing is a rapidly developing research area. Both reversible logic synthesis and testing reversible logic circuits are very important issues in this area. In this thesis, we present our work in these two aspects. We consider a new fault model, namely the crosspoint fault, for reversible circuits. The effects of this kind of fault on the behaviour of the circuits are studied. A randomized test pattern generation algorithm targeting this kind of fault is introduced and analyzed. The relationship between the crosspoint faults and stuck-at faults is also investigated. The crosspoint fault model is then studied for possible applications in reversible logic synthesis. One type of redundancy exists in Toffoli networks in the form of undetectable multiple crosspoint faults. So redundant circuits can be simplified by deleting those undetectable faults. The testability of multiple crosspoint faults is analyzed in detail. Several important properties are proved and integrated into the simplifying algorithm so as to speed up the process. We also provide an optimized implementation of a Reed-Muller spectra based reversible logic synthesis algorithm. This new implementation uses a compact form of the Reed-Muller spectra table of the specified reversible function to save memory during execution. Experimental results are presented to illustrate the significant improvement of this new implementation.

Orientador: Gilberto Pechoto de Melo / Banca: Amarildo Tabone Paschoalini / Banca: Yukio Kobayashi / Resumo: Nos últimos anos, tem havido um grande interesse das indústrias no desenvolvimento de novas técnicas de detecção e localização de falhas, pois se preocupam cada vez mais com a segurança, havendo assim, a necessidade de supervisão e monitoramento dos sistemas para que as falhas sejam evitadas ou sanadas o mais rápido possível. Determinados parâmetros em sistemas reais como massa, rigidez e amortecimento, podem variar devido ao aparecimento de falhas ou ao próprio desgaste natural dos componentes. Um aparecimento de trincas pode provocar perdas econômicas ou até conduzir a situações perigosas com paradas abruptas das máquinas e/ou equipamentos. Através do auxílio de modelos teóricos bem definidos, métodos de identificação de parâmetros, observadores de estado e auxílio à decisão foi possível desenvolver uma metodologia para detecção e localização de trincas em estruturas reticuladas, dando ênfase às tridimensionais. Foi possível detectar e localizar a trinca já no seu início e acompanhar sua propagação para uma possível parada programada. Foi utilizada a metodologia dos observadores de estado, que pode reconstruir os estados não medidos ou os valores provenientes de pontos de difícil acesso no sistema. Foi construída uma estrutura reticulada no Laboratório para validação da metodologia desenvolvida e os resultados foram bastante satisfatórios / Abstract: In recent years there has been a great interest of industry in developing new techniques for detection and location of faults, because they worry more about security, so there is the need for supervision and monitoring systems so that failures are avoided or remedied as soon as possible. Certain parameters in real systems such as mass, stiffness and damping can vary due to some failures to own or wear and tear of components. An appearance of cracks can cause economic loss or even lead to dangerous situations with abrupt stopping of machines and/or equipment. Through the aid of well-defined theoretical models, methods of parameter identification, state observers and aid the decision was possible to develop a methodology for detecting and locating cracks in frame structures, emphasizing the three-dimensional. It was possible to detect and locate the crack already in its early stages and monitor its spread to a possible shutdown. Methodology was applied for observer status, which can reconstruct the unmeasured states or values from points of difficult access in the system. A reticulated structure was built at the Laboratory for validation of the methodology and the results were very satisfactory / Mestre

The complexity of safety-related embedded computer systems is steadilyincreasing. Besides verifying that such systems implement the correct functionality, it is essential to verify that they also present an acceptable level of robustness. Robustness is in this thesis defined as the resilience of hardware, software or systems against errors that occur during runtime. One way of performing robustness assessment is to carry out fault injection, also known as fault insertion testing from certain safety standards. The idea behind fault injection is to accelerate the occurrence of faults in the system to evaluate its behavior under the influence of anticipated faults, and to evaluate error handling mechanisms. Model-based development is becoming more and more common for the development of safety-related software. Thus, in this thesis we investigate how we can benefit from conducting fault injection experiments on behavior models of software. This is defined as model-implemented fault injection in this thesis, since additional model artifacts are added to support the injection of faults that are activated during simulation. In particular, this thesis addresses injection of hardware fault effects (e.g. bit-level errors in microcontrollers) into Simulink® models. To evaluate the method, a fault injection tool has been developed (called MODIFI), that is able to perform fault injection into Simulink behavior models. MODIFI imports tailored fault libraries that define the effects of faults according to an XML-schema. The fault libraries are converted into executable model blocks that are added to behavior models and activated during runtime to emulate the effect of faults. Further, we use a method called minimal cut sets generation to increase the usefulness of the tool. During the work within MOGENTES, an EU 7th framework programme project that focused on model-based generation of test cases for dependable embedded systems, fault injection experiments have been performed on safety related models with the MODIFI tool. Experiments were also performed using traditional fault injection methods, and in particular hardware-implemented fault injection, to evaluate the correlation between the methods. The results reveal that fault injection on software models is efficient and useful for robustness assessment and that results produced with MODIFI appear to be representative for the results obtained with other fault injection methods. However, a software model suppresses implementation details, thus leading to fewer locations where faults can be injected. Therefore it cannot entirely replace traditional fault injection methods, but by performing model-implemented fault injection in early design phases an overview of the robustness of a model can be obtained, given these limitations. It can also be useful for testing of error handling mechanisms that are implemented in the behavior model. / QC 20111205

The popularity of MPI-based middleware and applications has led to their wide deployment. Such systems, however, are not inherently reliable and cannot tolerate external faults. This thesis presents a novel model-based approach for exploiting application features and other characteristics to categorize and create AEMs (Application Execution Model). This work realizes MPI/FT(tm), a middleware derived by selective incorporation of fault-tolerant features into MPI/Pro(tm) for two relevant AEMs. This thesis proves the following hypothesis: it is possible to successfully complete select MPI applications even in the presence of external faults, and such fault-tolerance can be achieved with acceptable performance overhead. This work defines parameters to measure the impact of this middleware on performance through faultree and fault-injected overheads. The hypothesis is validated through experimentation and measurement of sample MPI applications for two AEMs.

In this thesis two different problems in distributed sensor networks are considered. Part I involves optimal quantiser design for decentralised estimation of a two-state hidden Markov model with dual sensors. The notion of optimality for quantiser design is based on minimising the probability of error in estimating the hidden Markov state. Equations for the filter error are derived for the continuous (unquantised) sensor outputs (signals), which are used to benchmark the performance of the quantisers. Minimising the probability of filter error to obtain the quantiser breakpoints is a difficult problem therefore an alternative method is employed. The quantiser breakpoints are obtained by maximising the mutual information between the quantised signals and the hidden Markov state. This method is known to work well for the single sensor case. Cases with independent and correlated noise across the signals are considered. The method is then applied to Markov processes with Gaussian signal noise, and further investigated through simulation studies. Simulations involving both independent and correlated noise across the sensors are performed and a number of interesting new theoretical results are obtained, particularly in the case of correlated noise. In Part II, the focus shifts to the detection of faults in helicopter transmission systems. The aim of the investigation is to determine whether the acoustic signature can be used for fault detection and diagnosis. To investigate this, statistical change detection algorithms are applied to acoustic vibration data obtained from the main rotor gearbox of a Bell 206 helicopter, which is run at high load under test conditions.

Vacuum technology is ubiquitous in the high tech industries and scientific endeavors. Since vacuum pumps are critical to operation, semiconductor manufacturers desire reliable operations, ability to schedule downtime, and less costly maintenance services. To better cope with difficult maintenance issues, interests in novel fault diagnosis techniques are growing. This study concerns model based fault diagnosis and isolation (MB-FDI) of dry vacuum pumps in the semiconductor industry. Faults alter normal operation of a vacuum pump resulting in performance deviations, discovered by measurements. Simulations using an appropriate mathematical model with suitably chosen parameters can mimic faulty behavior. This research focuses on the construction of a detailed multi-stage dry vacuum pump model for MB-FDI, and the development of a simple and efficient FDI method to analyze common incipient faults such as particulate deposition and gas leak inside the pump. The pump model features 0-D thermo-fluid dynamics, scalable geometric representations of Roots blower, claw pumps and inter-stage port interfaces, a unified pipe model seamlessly connecting from free molecular to turbulent regimes, sophisticated internal leakage model considering true pump geometry and tribological aspects, and systematic assembly of a multi-stage configuration using single stage pump models. Design of a simple FDI technique for the dry vacuum pump includes staged fault simulations using faulty pump models, parametric study of faulty pump behaviors, and design of a health indicator based on classification. The main research contributions include the developments of an accurate multi-stage dry pump model with many features not found in existing pump models, and the design of a simple MB-FDI technique to detect and isolate the common faults found in dry vacuum pumps. The proposed dry pump model can pave the way for the future development of advanced MB-FDI methods, also performance improvement of existing dry vacuum pumps. The proposed fault classification charts can serve as a quick guideline for vacuum pump manufactures to isolate roots causes from faulty symptoms. / text

In this thesis two different problems in distributed sensor networks are considered. Part I involves optimal quantiser design for decentralised estimation of a two-state hidden Markov model with dual sensors. The notion of optimality for quantiser design is based on minimising the probability of error in estimating the hidden Markov state. Equations for the filter error are derived for the continuous (unquantised) sensor outputs (signals), which are used to benchmark the performance of the quantisers. Minimising the probability of filter error to obtain the quantiser breakpoints is a difficult problem therefore an alternative method is employed. The quantiser breakpoints are obtained by maximising the mutual information between the quantised signals and the hidden Markov state. This method is known to work well for the single sensor case. Cases with independent and correlated noise across the signals are considered. The method is then applied to Markov processes with Gaussian signal noise, and further investigated through simulation studies. Simulations involving both independent and correlated noise across the sensors are performed and a number of interesting new theoretical results are obtained, particularly in the case of correlated noise. In Part II, the focus shifts to the detection of faults in helicopter transmission systems. The aim of the investigation is to determine whether the acoustic signature can be used for fault detection and diagnosis. To investigate this, statistical change detection algorithms are applied to acoustic vibration data obtained from the main rotor gearbox of a Bell 206 helicopter, which is run at high load under test conditions.