This thesis defines the Energy Balance Load ( BL E ) as the difference between the heating requirements plus the electric gains in the building and the cooling coil loads. It then applies a first law energy balance in conjunction with the concepts of analytical redundancy (AR) and trend checking to demonstrate that measured values of BL E can be compared with the simulated characteristic ambient temperature-based BL E to serve as a useful tool to identify bad data. Uncertainty and sensitivity analysis are introduced to analyze the impact of each building or system parameter to the simulated values of BL E . A Visual Basic for Application (VBA) program has been developed through this research work, which applies the methodology illustrated in this thesis to automatically prescreen the measured building energy consumption data with the inputs of several key parameters. Through case studies of six on-campus buildings, the methodology and the program successfully identified monitored consumption data that appears to be erroneous, which may result from incorrect scale factors of the sensors and the operational changes to the building that may enormously affect the key parameters as the simulation inputs. Finally, suggestions are given for the on-line diagnostics of sensor signals.

Freeway work zones with lane closures can adversely affect mobility, safety, and sustainability. Capacity drop phenomena near work zone areas can further decrease work zone capacity and exacerbate traffic congestion. To mitigate the negative impacts caused by freeway work zones, many variable speed limits (VSL) control methods have been proposed to proactively regulate the traffic flow. However, a simple yet robust VSL controller that considers the nonlinearity induced by the associated capacity drop is still needed. Also, most existing studies of VSL control neglected the impacts of traffic sensor failures that commonly occur in transportation systems. Large deviations of traffic measurements caused by sensor faults can greatly affect the reliability of VSL controllers. To address the aforementioned challenges, this research proposes a fault-tolerant VSL controller for a freeway work zone with consideration of sensor faults. A traffic flow model was developed to understand and describe the traffic dynamics near work zone areas. Then a VSL controller based on sliding mode control was designed to generate dynamic speed limits in real time using traffic measurements. To achieve VSL control fault tolerance, analytical redundancy was exploited to develop an observer-based method and an interacting multiple model with a pseudo-model set (IMMP) based method for permanent and recurrent sensor faults respectively. The proposed system was evaluated under realistic freeway work zone conditions using the traffic simulator SUMO. This research contributes to the body of knowledge by developing fault-tolerant VSL control for freeway work zones with reliable performance under permanent and recurrent sensor faults. With reliable sensor fault diagnosis, the fault-tolerant VSL controller can consistently reduce travel time, safety risks, emissions, and fuel consumption. Therefore, with a growing number of work zones due to aging road infrastructure and increasing demand, the proposed system offers broader impacts through congestion mitigation and consistent improvements in mobility, safety, and sustainability near work zones. / Thesis / Doctor of Philosophy (PhD) / Freeway work zones can increase congestion with higher travel time, safety risk, emissions and fuel consumption. This research aims to improve traffic conditions near work zones using a variable speed limits control system. By exploiting redundant traffic information, a variable speed limit control system that is insensitive to traffic sensor failures is presented. The proposed system was evaluated under realistic freeway work zone conditions in a simulation environment. The results show that the proposed system can reliably detect sensor failures and consistently provide improvements in mobility, safety and sustainability despite the presence of traffic sensor failures.

A diagnosis procedure is an algorithm to detect and locate (isolate) faulty components in a dynamic process. In 1994 the California Air Resource Board released a regulation, called OBD II, demanding a thorough diagnosis system on board automotive vehicles. These legislative demands indicate that diagnosis will become increasingly important for automotive engines in the next few years. To achieve diagnosis, redundancy has to be included in the system. This redundancy can be either hardware redundancy or analytical redundancy. Hardware redundancy, e.g. an extra sensor or extra actuator, can be space consuming or expensive. Methods based on analytical redundancy need no extra hardware, the redundancy here is generated from a process model instead. In this thesis, approaches utilizing analytical redundancy is examined. A literature study is made, surveying a number of approaches to the diagnosis problem. Three approaches, based on both linear and non-linear models, are selected and further analyzed and complete design examples are performed. A mathematical model of an SI-engine is derived to enable simulations of the designed methods.

L'objectif est de cette thèse est de développer, de tester puis d'implémenter des schémas de surveillance et d'estimation des paramètres essentiels aux pilotes et aux lois embarquées, offrant ainsi une alternative et un complément aux signaux mesures par les capteurs. Les méthodes développées au cours de la thèse ont donc plusieurs finalités applicatives : estimer les états avion ainsi que des paramètres externes (comme le vent et les erreurs de modélisation), détecter la défaillance d'un ou plusieurs capteurs lorsqu'un dysfonctionnement se produit, et enfin s'adapter à cette dégradation de manière à continuer à délivrer des estimées exploitables par les systèmes sur un horizon temporel plus ou moins long.D'un point de vue pratique, dans le domaine de la détection, on cherchera à ce que le processus de détection d'une panne soit capable : 1) de distinguer une faute sur les sondes d'incidence d'une faute sur le paramètre de vitesse conventionnelle ou d’une erreur sur la masse renseignée par le pilote dès le début du vol. Une faute sur un de ces paramètres est aujourd'hui détectée sans qu'une isolation de la source ne soit possible 2) d'identifier des modes communs de panne, c'est-à-dire un embarquement simultané cohérent de plusieurs capteurs mesurant le même paramètre. La redondance matérielle utilisée aujourd'hui ne permet pas de détecter un embarquement simultané cohérent de deux ou trois capteurs 3) de sélectionner les sources toujours valides lorsque le schéma de vote majoritaire détecte une faute d'un capteur. Le schéma actuellement en usage sur avion combine les sources redondantes pour délivrer une mesure consolidée. En cas d'invalidation de celle-ci suite à la perte d'au moins deux capteurs, il est en effet possible que le troisième soit toujours valide et puisse être utilisé pour le reste du vol.Les bénéfices potentiels à plus long terme se situent donc dans l'amélioration des performances (en réduisant par exemple le nombre de commutations de lois), et dans la diminution de la charge de travail des pilotes en accroissant encore la disponibilité des fonctions de haut niveau destinées à les seconder et à alléger leur tâche (protections du domaine de vol, pilote automatique, etc.). La détection de modes communs de panne participera aussi à augmenter encore la sécurité en vol. / The improvement of the aircraft performance and the decrease of the pilots’workload require more complex new aircraft avionic systems. This complexificationpaves the way for new constraints, such as improving the availability of the most criticalflight parameters used by the pilots (mainly the calibrated airspeed) or by themost advanced flight control systems (as the angle of attack, the altitude pressureor the aircraft weight). Today, their availability is mainly guaranteed by mean of astrong hardware redundancy (triplex type for civil aircraft). However, this solution isperfectible and penalizes the overall system performances in terms of weight, powerconsumption, space requirements and extra maintenance needs. Some faults, suchas common mode failures, which correspond to simultaneous and consistent faultsof at least two sensors measuring a same variable, cannot be detected. In this thesis,a solution based on the principle of the so-called analytical redundancy has beendeveloped to detect them and reconstruct through time the missing signals. Thissolution depends on the measurements, and kinematic and flight dynamic equationsavailable. For instance, the lift force equation combines numerous flight parametersof interest. It is of great value in the data fusion process on condition of having anaccurate surrogate model (as lookup tables adjusted with flight data, neural network,etc.) to estimate the lift force coefficient. In the end, an extended Kalmanfilter has been developed to estimate the critical longitudinal flight parameters. Besides,the existing complementarity between this model-based approach and severalsignal-based methods has permitted to have sensor faults and weight error diagnosisalong with unitary sensor validation capabilities. The architecture and its relatedembedded algorithms finally developed have been done with respect to the strongindustrial constraints (particularly in term of computation burden and formalism).They have been validated using simulation and flight data sets, especially for theisolation of slow drift common mode failures as they represent today the most challengingsensor faults to detect.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-24T15:00:02Z No. of bitstreams: 1 Ronnie.pdf: 3320281 bytes, checksum: 56be4f928c1366ece428d2ae6caf9627 (MD5) / Made available in DSpace on 2017-08-24T15:00:02Z (GMT). No. of bitstreams: 1 Ronnie.pdf: 3320281 bytes, checksum: 56be4f928c1366ece428d2ae6caf9627 (MD5) Previous issue date: 2012-08-31 / This work aims to monitor and analyze the data from the instrumentation system of a substation as a way to identify false alarms, which can result in a decision by the mistaken maintenance and operation. This project was conceived because of the need for a research and development project which is called Maintenance Management Center (MMC) whose overall objective is to assist in the maintenance of their equipment operational intervention. Data is extracted from the automation system that has digital relay protection function and measurement of the electric grid, passing through a sequence of data processing to achieve the results that will serve for the detection and diagnosis of faults. We applied methods based on quantitative model by transforming the data system of continuous variables (SVC) and qualitative data by transforming the system of discrete event (SDE) applying analytical redundancy techniques and neural networks respectively, thus aiming a simplified model for detection and diagnosis fault (DDF). The model has been designed taking into account the characteristics DDF due to its stages, thereby providing a good system failure recovery. Know filter if certain event is real or a false alarm is not an easy task, but this system will have to meet this purpose. Technological resources are used fairly consolidated in the industrial process for the integration of the solution, because the time factor and information processing are critical in the results generated by the system recovery. Another key point of this trial was to have developed a system based on experiential knowledge, because it has higher robustness in results. / Este trabalho tem como objetivo monitorar e analisar os dados provenientes do sistema de instrumentação de uma subestação como forma de identificar falsos alarmes, que pode acarretar em uma tomada de decisão equivocada por parte da manutenção e operação. Este projeto foi concebido devido à necessidade de um projeto de pesquisa e desenvolvimento que se intitula Centro de Gestão da Manutenção (CGM) cujo objetivo global é auxiliar a manutenção na intervenção operacional de seus equipamentos. Os dados são extraídos do sistema de automação provenientes dos reles digitais que tem função de proteção e medição da rede elétrica, passando por um sequencia de transformação dos dados até chegar aos resultados, que servirá para detecção e diagnostico de falhas. Foram aplicados métodos baseados no modelo quantitativo através da transformação dos dados do sistema de variáveis contínuas (SVC) e qualitativo através da transformação dos dados do sistema de eventos discretos (SED) aplicando técnicas de redundância analítica e redes neurais respectivamente, objetivando assim um modelo simplificado para detecção e diagnóstico da falha (DDF). O modelo foi concebido levando em consideração as características DDF decorrente de suas etapas, propiciando assim um bom sistema de recuperação de falha. Saber filtrar se determinado evento é real ou um falso alarme não é uma tarefa fácil, porém este sistema terá que atender este propósito. Foram utilizados recursos tecnológicos bastante consolidados no processo industrial para garantir a integração da solução, pois o fator tempo e o processamento da informação são decisivos nos resultados gerados pelo sistema de recuperação. Outro ponto fundamental neste trabalho foi ter desenvolvido um sistema baseado no conhecimento experimental, pois se tem maior robustez nos resultados.

Les travaux présentés dans ce mémoire contribuent à la définition de méthodes pour la détection et le diagnostic de défauts affectant les systèmes aéronautiques. Un système représentatif sert de support d'étude, constitué du modèle non linéaire à six degrés de liberté d'un missile intercepteur, de ses capteurs et actionneurs ainsi que d'une boucle de guidage-pilotage. La première partie est consacrée au développement de deux méthodes de diagnostic exploitant l'information de commande en boucle fermée et les caractéristiques des modèles aéronautiques. La première méthode utilise les objectifs de commande induits par les lois de guidage-pilotage pour générer des résidus indiquant la présence de défauts. Ceci permet la détection des défauts sur les actionneurs et les capteurs, ainsi que leur localisation pour ces derniers. La deuxième méthode exploite la mesure de dérivées des variables d'état (via une centrale inertielle) pour estimer la valeur de la commande réalisée par les actionneurs, sans intégration du modèle non linéaire du système. Le diagnostic est alors effectué en comparant cette estimée avec la valeur désirée, ce qui permet la détection, la localisation et l'identification de défauts multiples sur les actionneurs.La seconde partie propose une méthodologie de réglage automatique des paramètres internes (les hyperparamètres) de méthodes de diagnostic. Ceci permet une comparaison plus objective entre les méthodes en évaluant la meilleure performance de chacune. Le réglage est vu comme un problème d'optimisation globale, la fonction à optimiser étant calculée via la simulation numérique (potentiellement coûteuse) de cas test. La méthodologie proposée est fondée sur un métamodèle de krigeage et une procédure itérative d’optimisation bayésienne, qui permettent d’aborder ce problème à faible coût de calcul. Un nouvel algorithme est proposé afin d'optimiser les hyperparamètres d'une façon robuste vis à vis de la variabilité des cas test pertinents.Mots clés : détection et diagnostic de défauts, guidage-pilotage, krigeage, minimax continu, optimisation globale, redondance analytique, réglage automatique, systèmes aéronautiques. / This manuscript reports contributions to the development of methods for fault detection and diagnosis applied to aeronautical systems. A representative system is considered, composed of the six-degree-of-freedom nonlinear model of a surface-to-air missile, its sensors, actuators and the associated GNC scheme. The first part is devoted to the development of two fault diagnosis approaches that take advantage of closed-loop control information, along with the characteristics of aeronautical models. The first method uses control objectives resulting from guidance laws to generate residuals indicative of the presence of faults. This enables the detection of both actuator and sensor faults, and the isolation of sensor faults. The second method exploits the measurement of derivatives of state variables (as provided by an IMU) to estimate the control input as achieved by actuators, without the need to integrate the nonlinear model. Detection, isolation and identification of actuator faults can then be performed by comparing this estimate with the desired control input.The second part presents a new automatic-tuning methodology for the internal parameters (the hyperparameters) of fault diagnosis methods. This allows a fair comparison between methods by evaluating their best performance. Tuning is formalised as the global optimization of a black-box function that is obtained through the (costly) numerical simulation of a set of test cases. The methodology proposed here is based on Kriging and Bayesian optimization, which make it possible to tackle this problem at a very reduced computational cost. A new algorithm is developed to address the optimization of hyperparameters in a way that is robust to the variability of the test cases of interest.

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Cette thèse développe des approches pour le diagnostic et le pronostic de systèmes dynamiques incertains en utilisant la technique de modélisation Bond Graph (BG). Tout d'abord, une représentation par intervalles des incertitudes paramétriques et de mesures est intégrée à un modèle BG-LFT (Linear Fractional Transformation). Une méthode de détection robuste de défaut est développée en utilisant les règles de l'arithmétique d'intervalle pour la génération de seuils robustes et adaptatifs sur les résidus nominaux. La méthode est validée en temps réel sur un système de générateur de vapeur.Deuxièmement, une nouvelle méthodologie de pronostic hybride est développée en utilisant les Relations de Redondance Analytique déduites d'un modèle BG et les Filtres Particulaires. Une estimation de l'état courant du paramètre candidat pour le pronostic est obtenue en termes probabilistes. La prédiction de la durée de vie résiduelle est atteinte en termes probabilistes. Les incertitudes associées aux mesures bruitées, les conditions environnementales, etc. sont gérées efficacement. La méthode est validée en temps réel sur un système mécatronique incertain.Enfin, la méthodologie de pronostic développée est mise en œuvre et validée pour le suivi efficace de la santé d'un sous-système électrochimique d’une pile à combustible à membrane échangeuse de protons (PEMFC) industrielle à l’aide de données de dégradation réelles. / This thesis develops the approaches for diagnostics and prognostics of uncertain dynamic systems in Bond Graph (BG) modeling framework. Firstly, properties of Interval Arithmetic (IA) and BG in Linear Fractional Transformation, are integrated for representation of parametric and measurement uncertainties on an uncertain BG model. Robust fault detection methodology is developed by utilizing the rules of IA for the generation of adaptive interval valued thresholds over the nominal residuals. The method is validated in real time on an uncertain and highly complex steam generator system.Secondly, a novel hybrid prognostic methodology is developed using BG derived Analytical Redundancy Relationships and Particle Filtering algorithms. Estimations of the current state of health of a system parameter and the associated hidden parameters are achieved in probabilistic terms. Prediction of the Remaining Useful Life (RUL) of the system parameter is also achieved in probabilistic terms. The associated uncertainties arising out of noisy measurements, environmental conditions etc. are effectively managed to produce a reliable prediction of RUL with suitable confidence bounds. The method is validated in real time on an uncertain mechatronic system.Thirdly, the prognostic methodology is validated and implemented on the electrical electro-chemical subsystem of an industrial Proton Exchange Membrane Fuel Cell. A BG of the latter is utilized which is suited for diagnostics and prognostics. The hybrid prognostic methodology is validated, involving real degradation data sets.