Evaluating the bovine tuberculosis eradication mechanism and its risk factors in England’s cattle farms

Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms

A multidimensional Bayesian architecture for real-time anomaly detection and recovery in mobile robot sensory systems

peer reviewedFor mobile robots to operate in an autonomous and safe manner they must be able to adequately perceive their environment despite challenging or unpredictable conditions in their sensory apparatus. Usually, this is addressed through ad-hoc, not easily generalizable Fault Detection and Diagnosis (FDD) approaches. In this work, we leverage Bayesian Networks (BNs) to propose a novel probabilistic inference architecture that provides generality, rigorous inferences and real-time performance for the detection, diagnosis and recovery of diverse and multiple sensory failures in robotic systems. Our proposal achieves all these goals by structuring a BN in a multidimensional setting that up to our knowledge deals coherently and rigorously for the first time with the following issues: modeling of complex interactions among the components of the system, including sensors, anomaly detection and recovery; representation of sensory information and other kinds of knowledge at different levels of cognitive abstraction; and management of the temporal evolution of sensory behavior. Real-time performance is achieved through the compilation of these BNs into feedforward neural networks. Our proposal has been implemented and tested for mobile robot navigation in environments with human presence, a complex task that involves diverse sensor anomalies. The results obtained from both simulated and real experiments prove that our architecture enhances the safety and robustness of robotic operation: among others, the minimum distance to pedestrians, the tracking time and the navigation time all improve statistically in the presence of anomalies, with a diversity of changes in medians ranging from ≃20% to ≃500%

Identifying evidences of computer programming skills through automatic source code evaluation

Orientador: Roberto PereiraCoorientador: Eleandro MaschioTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/03/2020Inclui referências: p. 98-106Área de concentração: Ciência da ComputaçãoResumo: Esta tese e contextualizada no ensino de programacao de computadores em cursos de Computacao e investiga aspectos e estrategias para avaliacao automatica e continua de codigos fonte desenvolvidos pelos alunos. O estado da arte foi identificado por meio de revisao sistematica de literatura e revelou que as pesquisas anteriores tendem a realizar avaliacoes baseadas em aspectos tecnicos de codigos fonte, como a avaliacao de corretude funcional e a deteccao de erros. Avaliacoes baseadas em habilidades, por outro lado, sao pouco exploradas e possuem potencial para fornecer detalhes a respeito de habilidades representadas por conceitos de alto nivel, como desvios condicionais e estruturas de repeticao. Um metodo de identificacao automatica de evidencias de aprendizado e entao proposto como uma abordagem baseada em habilidades para a avaliacao automatica de codigos fonte de programacao. O metodo e caracterizado pela implementacao de diferentes estrategias para avaliacao de codigos fonte, identificacao de evidencias de habilidades de programacao, e representacao destas habilidades em um modelo do aluno. Experimentos realizados em ambientes controlados (bases de dados artificiais) mostraram que estrategias automaticas de avaliacao de codigo fonte sao viaveis. Experimentos conduzidos em ambientes reais (codigos fonte produzidos por alunos) produziram resultados semelhantes aos ambientes controlados, entretanto revelaram limitacoes relacionadas a implementacao das estrategias, como vulnerabilidades a sintaxes inesperadas e falhas em expressoes regulares. Um conjunto de habilidades foi selecionado para compor o modelo do aluno, representado por uma rede bayesiana dinamica. Por meio de experimentos foi demonstrado que a alimentacao do modelo com evidencias resultantes da avaliacao automatica de codigos fonte permite o acompanhamento do progresso das habilidades dos alunos. Finalmente, as estrategias automaticas em conjunto com os recursos do modelo do aluno permitiram a demonstracao da avaliacao baseada em habilidades, que se mostrou um recurso valioso para identificacao de solucoes funcionalmente corretas, porem conceitualmente incorretas; quando o programa e funcionalmente correto, retornando resultados esperados a determinadas entradas, porem foi construido com recursos e conceitos incorretos. Palavras-chave: Programacao de Computadores, Avaliacao Automatica, Avaliacao Baseada em HabilidadesAbstract: This thesis is contextualized in the teaching of computer programming in Computing courses and investigates aspects and strategies for automatic and continuous evaluation of student developed source codes. The state of the art was identified through systematic literature review and revealed previous research tends to perform evaluations based on source codes technical aspects, such as functional correctness assessment and error detection. Skills-based assessments, in turn, are less explored although having potential to provide details of skills represented by high-level concepts, such as conditionals and repetition structures. A method for automatic identification of learning evidences is then proposed as a skills-based approach to automatic evaluation of programming source codes. The method is characterized by implementing different strategies for source code evaluation, identifying evidences of programming skills, and representing these skills in a student model. Experiments conducted in controlled scenarios (testing datasets) have shown automatic source code evaluation strategies are viable. Experiments conducted in real scenarios (student-made source codes) produced results similar to controlled scenarios, however, implementation-related limitations were revealed for some strategies, such as vulnerabilities to unexpected syntax and flaws in regular expressions. A skill set was selected to compose our student model, represented by a Dynamic Bayesian Network. Experiments have shown feeding the model with evidences resulting from source codes automatic evaluation allows monitoring students' skills progress. Finally, automatic strategies coupled with student model capabilities enabled demonstrating skills-based assessment, which showed a valuable resource for identifying functionally correct source codes, but conceptually incorrect; when a program is correct functionally, returning expected results to specific inputs, but it was built with erroneous concepts and resources. Keywords: Computer Programming, Automatic Evaluation, Skills-Based Assessmen

Research on Control Loop Fault Diagnosis

控制回路故障诊断旨在提高工业生产装置操作的安全性与可靠性，一直受到学术界与工业界的广泛关注。工业过程控制回路涉及多种复杂问题，其故障可能存在多种形式。论文研究控制回路中模型预测控制（MPC）的模型失配故障、回路振荡故障，以及基于数据驱动的贝叶斯故障诊断。针对工业现场的实际问题，提出了外加测试信号的模型误差诊断，基于频域分析的回路振荡监测，和基于期望极大化（EM）算法的贝叶斯故障诊断方法。本文具体研究内容如下： 针对MPC模型失配问题，提出了基于外加低幅正弦测试信号的模型诊断方法。通过获取过程三个频率点上的精确频率响应并与当前MPC模型比较，加权形成模型误差矩阵；给出模型诊断误差上界概念，估计...Control loop fault diagnosis deals with the safety and consistency of control loop operation, thus receiving increasing attention in both academic research and industrial application. Since process control systems are complex, usually faults may occur in different components and represent in different behaviors. This thesis is concerned with different faults related to control system, which includ...学位：工学博士院系专业：信息科学与技术学院_控制理论与控制工程学号：2322011015411

Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523TESI

Development of a prognostic model for Macrophage Activation Syndrome in Systemic Juvenile Idiopathic Arthritis

Introduction: Macrophage activation syndrome (MAS) is a potentially life-threatening complication of systemic juvenile idiopathic arthritis (SJIA) characterized by heterogeneous organ involvement and severity. Early identification of patients at high risk of complicated clinical course may improve outcome by helping initiate prompt, appropriate immunosuppressive and supportive treatments. Yet, despite recent progress in clarifying the underlying immunological mechanisms, factors driving organ damage and severe outcome are not entirely understood, nor has the prognostic value of routinely gathered clinical and laboratory factors been fully explored. Objectives: To develop a prognostic model for SJIA-MAS based on routinely available parameters at disease onset, accounting for patient heterogeneity, possible latent factors, non-linear relationships and confounders. Methods: We examined a retrospective multinational cohort of 362 patients diagnosed with SJIA-MAS. The relationships between demographic, laboratory features at MAS onset (such as hemoglobin, whole blood cells, platelets, ERS, CRP, AST, ALT, bilirubin, fibrinogen, d-dimer, ferritin and creatinine), therapeutic interventions and outcomes were analyzed. Outcomes of interest included a \u201csevere course\u201d (defined as ICU admission or death), occurring of organs failure and CSN dysfunction. To identify potential phenotypes related to clinical features and outcome, we explored laboratory parameter patterns at MAS onset through Latent class modeling, which detects multiple unobserved clusters in heterogeneous populations. A structural causal approach was then used for investigating causal pathways leading to severe outcomes. Directed acyclic graphs (DAGs) were employed to depict possible causal relationships between the candidate biomarkers, potential confounding variables, and the outcomes, and inform the choice of adjustment sets in multivariate regression models. We assessed the possible relationships between variables and outcomes by penalized likelihood logistic regression and identified optimal cut off points for prognostic factors using Multiple Adaptive Regression Splines (MARS) and Classification and Regression Trees (CART). To account for possible treatment confounders, the effect of cyclosporine and etoposide use on outcomes was estimated using augmented inverse probability weighting (IPW) with double robust methods. Finally, results from previous analyses were incorporated in a probabilistic framework through a Bayesian network (BN) model, which provides risk estimates for specific clinical scenarios and quantifies the amount of information contributed from the identified prognostic variables. Results: The latent class model revealed six clusters based on biomarkers at MAS onset, characterized by the following features: mild alterations of white blood cells, platelets, fibrinogen, d-dimer and ferritin values, considered the baseline type (cluster 1, n =115); hyperferritinemia with low organs involvement (cluster 2, n = 101); elevation of inflammatory markers (cluster 3, n =51); hepatobiliary involvement (cluster 4, n = 41); severe pancytopenia, liver and kidney failure with higher elevation of LDH, d-dimer, ferritin (cluster 5, n = 30); biliary and renal dysfunction (cluster 6, n = 24). Cluster 2 and 3 presented lower age and SJIA duration at MAS onset compared to other subgroups. Cluster membership was predictive of severe course (p<0.001), CSN involvement (p<0.001), Hemorrhagic complications (p <0.001) and Heart failure (p<0.001), with patients in cluster 5 showing the highest risk of severe course and heart failure, and increased occurrence of CNS and Hemorrhagic manifestations in both cluster 5 and 6. In multivariate regression models, parameters at onset associated with risk of severe course were creatinine (OR 1,6 [95% CI 1.13\u20132.3]; p = 0.008) and albumin levels (OR 0,65 [95% CI 0.44\u20130.98]; p = 0.044) Higher risk of CNS involvement was found for patients younger at MAS onset (OR 0,62 [95% CI 0.42\u20130.92]; p = 0.018). Na (OR 0.0,89 [95% CI 0.82\u20130.96]; p = 0.006) and creatinine values (OR 1.69 [95% CI 1.14\u20132.5]; p = 0.009) were identified as independent predictors of mortality. There was no evidence for an effect of etoposide (OR 1.03 [95% CI 0.91\u20131.12]) and cyclosporine (OR 1.04 [95% CI 0.92\u20131.19]) on severe course. BNs defined distinct groups with different probability of severe outcomes, achieving a c-index of 0.76 for mortality, 0.81 for severe course and 0.81 for CNS involvement. Adding the obtained latent clusters to the BN model increased the prediction accuracy for severe course up to a c-index of 0.83. Based on information theory metrics (mutual information) from the BN model, decision algorithms for each outcome and a web-based decision support tool for external users were implemented. Conclusions: We developed a probabilistic prognostic model of SJIA-MAS based on routinely available data. This stratification tool may facilitate informed decision-making about the clinical management of these patients. The probabilistic and information-theoretic approach offers a framework for further validation, expansion and integration of the model with emerging molecular biomarkers

Hand tracking and bimanual movement understanding

Bimanual movements are a subset ot human movements in which the two hands move together in order to do a task or imply a meaning A bimanual movement appearing in a sequence of images must be understood in order to enable computers to interact with humans in a natural way This problem includes two main phases, hand tracking and movement recognition. We approach the problem of hand tracking from a neuroscience point ot view First the hands are extracted and labelled by colour detection and blob analysis algorithms In the presence of the two hands one hand may occlude the other occasionally Therefore, hand occlusions must be detected in an image sequence A dynamic model is proposed to model the movement of each hand separately Using this model in a Kalman filtering proccss the exact starting and end points of hand occlusions are detected We exploit neuroscience phenomena to understand the beha\ tour of the hands during occlusion periods Based on this, we propose a general hand tracking algorithm to track and reacquire the hands over a movement including hand occlusion The advantages of the algorithm and its generality are demonstrated in the experiments. In order to recognise the movements first we recognise the movement of a hand Using statistical pattern recognition methods (such as Principal Component Analysis and Nearest Neighbour) the static shape of each hand appearing in an image is recognised A Graph- Matching algorithm and Discrete Midden Markov Models (DHMM) as two spatio-temporal pattern recognition techniques are imestigated tor recognising a dynamic hand gesture For recognising bimanual movements we consider two general forms ot these movements, single and concatenated periodic We introduce three Bayesian networks for recognising die movements The networks are designed to recognise and combinc the gestures of the hands in order to understand the whole movement Experiments on different types ot movement demonstrate the advantages and disadvantages of each network

Damage Precursor Based Structural Health Monitoring and Prognostic Framework Using Dynamic Bayesian Network

Structural health monitoring (SHM), as an essential tool to ensure the health integrity of aging structures, mostly focus on monitoring conventional observable damage markers such as fatigue crack size. However, degradation starts and progressively evolves at microstructural levels much earlier than detection of such indicators. This dissertation goes beyond classical approaches and presents a new SHM framework based on evolution of Damage Precursors, when conventional direct damage indicator, such as crack, is unobservable, inaccessible or difficult to measure. Damage precursor is defined in this research as “any detectable variation in material/ physical properties of the component that can be used to infer the evolution of the hidden/ inaccessible/ unmeasurable damage during the degradation”. Accordingly, the degradation process is to be expressed based on progression of damage precursor through time and the damage state assessment would be updated by incorporating multiple different evidences. Therefore, this research proposes a systematic integration approach through Dynamic Bayesian Network (DBN) to include all the evidences and their relationships. The implementation of augmented particle filtering as a stochastic inference method inside DBN enables estimating both model parameters and damage states simultaneously in light of various evidences. Incorporating different sources of information in DBN entails advance techniques to identify and formulate the possible interaction between potentially non-homogenous variables. This research uses the Support Vector Regression (SVR) in order to define generally unknown nonparametric and nonlinear correlation between some of the variables in the DBN structure. Additionally, the particle filtering algorithm is studied more fundamentally in this research and a modified approach called “fully adaptive particle filtering” is proposed with the idea of online updating not only the state process model but also the measurement model. This new approach improves the ability of SHM in real-time diagnostics and prognostics. The framework is successfully applied to damage estimation and prediction in two real-world case studies of 1) crack initiation in a metallic alloy under fatigue and, 2) damage estimation and prognostics in composite materials under fatigue. The proposed framework is intended to be general and comprehensive such that it can be implemented in different applications

A Bayesian Approach to Sensor Placement and System Health Monitoring

System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all the required information reliably at all times. The increasing costs of aging systems and infrastructures have become a major concern, and system health monitoring techniques can ensure increased safety and reliability of these systems. Similar concerns also exist for newly designed systems. The main objectives of this research were: (1) to find an effective way for optimal functional sensor placement under uncertainty, and (2) to develop a system health monitoring approach with both prognostic and diagnostic capabilities with limited and uncertain information sensing and monitoring points. This dissertation provides a functional/information --based sensor placement methodology for monitoring the health (state of reliability) of a system and utilizes it in a new system health monitoring approach. The developed sensor placement method is based on Bayesian techniques and is capable of functional sensor placement under uncertainty. It takes into account the uncertainty inherent in characteristics of sensors as well. It uses Bayesian networks for modeling and reasoning the uncertainties as well as for updating the state of knowledge for unknowns of interest and utilizes information metrics for sensor placement based on the amount of information each possible sensor placement scenario provides. A new system health monitoring methodology is also developed which is: (1) capable of assessing current state of a system's health and can predict the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have or are likely to cause system failure or degradation (diagnosis). It can also be set up as a dynamic monitoring system such that through consecutive time steps, the system sensors perform observations and send data to the Bayesian network for continuous health assessment. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation)