A number of non-invasive fault detection and diagnosis (FDD) techniques have been researched and have proven to have worked well in classifying faults in internal combustion engines (ICE) and other mechanical and electrical systems. These techniques are an integral step to creating more robust and accurate methods of determining where or how a fault has or will occur in such systems. These FDD techniques have the potential to not only save time avoiding a tear-down of a costly machine, but could potentially add another layer of safety in detecting and diagnosing a fault much earlier than was possible before. Looking at the previous research methods and the systems they used to acquire this data, it is a natural progression to try and make a system which is able to encapsulate all of these ideologies into one inexpensive module capable of integrating itself into the advanced set of FDD. This thesis follows along with the development of a new wireless sensor that is developed specifically for the use in FDD for ICE and other mechanical systems. A new set of software and firmware is created for the system to be able to be incorporated into previously designed algorithms. After creating and manufacturing the sensor it is put to the test by incorporating it into several Artificial Neural Networks (ANN) and comparing the results to previous experiments done with previous research equipment. Using vibration data acquired from a running engine to train a neural network, the wireless sensor was able to perform equally as well as its expensive counter parts. It proved to have the ability to achieve 100% accuracy in classifying specific engine faults. The performance of three ANN training algorithms, Levenberg-Marquardt (LM), extended Kalman Filter (EKF), and Smooth Variable Structure filter (SVSF), were tested and compared. Adding to the feasibility of a standalone system the wireless sensor was tested in a live environment as a method of instant ICE fault detection. / Thesis / Master of Applied Science (MASc)

The diesel engine in heavy-duty trucks is a complex system with many components working together, and a malfunction in any of these components can impact engine performance and result in increased emissions. Fault detection and diagnosis have therefore become essential in modern vehicles, ensuring optimal performance and compliance with progressively stricter legal requirements. One of the most common faults in a diesel engineis faulty injectors, which can lead to fluctuations in the amount of fuel injected. Detecting these issues is crucial, prompting a growing interest in exploring additional signals beyond the currently used signal to enhance the performance and robustness of diagnosing this fault. In this work, an investigation was conducted to identify signals that correlate with faulty injectors causing over- and underfueling. It was found that the NOx, O2, and exhaust pressure signals are sensitive to this fault and could potentially serve as additional diagnostic signals. With these signals, two different diagnostic methods were evaluated to assess their effectiveness in detecting injector faults. The methods evaluated were data-driven residuals and Random Forest classifier. The data-driven residuals, when combined with the CUSUM algorithm, demonstrated promising results in detecting faulty injectors. The O2 signal proved effective in identifying both fault instances, while NOx and exhaust pressure were more effective at detecting overfueling. The Random Forest classifier also showed good performance in detecting both over- and underfueling. However, it was observed that using a classifier requires more extensive data preprocessing. Two preprocessing methods were employed: integrating previous measurements and calculating statistical measures over a defined time span. Both methods showed promising results, with the latter proving to be the better choice. Additionally, the generalization capabilities of these methods across different operating conditions were evaluated. It was demonstrated thatthe data-driven residuals yielded better results compared to the classifier, which requiredtraining on new cases to perform effectively.

In today's industrial landscape, the pursuit of operational excellence has driven organizations to seek innovative approaches to ensure the uninterrupted functionality of machinery and equipment. Predictive maintenance (PM) provides a pivotal strategy to achieve this goal by detecting faults earlier and predicting maintenance before the system enters a critical state. This thesis proposed a fault detection and diagnosis (FDD) method for predictive maintenance using particle filter resampling and a particle tracking technique. To develop this FDD method, particle filter and hidden Markov model efficiency in the forecasting system state variables are studied on a hydraulic wind power transfer system with different noise levels and system faults. Furthermore, a particle tracker is developed to analyze the particle filter's resampling process and study the particle selection process. After that, the proposed FDD method was developed and validated through three simulation tests employing system degradation models. Furthermore, the system's remaining useful life (RUL) is estimated for those simulation tests.

Machine health and condition monitoring are billion-dollar concerns for industry. Quality control and continuous improvement are some of the most important factors for manufacturers to consider in order to maintain a successful business. When work floor interruptions occur, engineers frequently employ “Band-Aid” fixes due to resource, timing, or technical constraints without solving for the root cause. Thus, a need for quick, reliable, and accurate fault detection and diagnosis methods are required. Within complex rotating machinery, a fundamental component that accounts for large amounts of downtime and failure involves a very basic yet crucial element, the rolling-element bearing. A worn-out bearing constitutes to some of the most drastic failures in any mechanical system next to electrical failures associated with stator windings. The cyclical motion provides a way for measurements to be taken via vibration sensors and analyzed through signal processing techniques. Methods will be discussed to transform these acquired signals into usable input data for neural network training in order to classify the type of fault that is present within the system. With the wide-spread utilization and adoption of neural networks, we turn our attention to the growing field of sequence-to-sequence deep learning architectures. Language based models have since been adapted to a multitude of tasks outside of text translation and word prediction. We now see powerful Transformers being used to accomplish generative modeling, computer vision, and anomaly detection -- spanning across all industries. This research aims to determine the efficacy of the Transformer neural network for use in the detection and classification of faults within 3-phase induction motors for the automotive industry. We require a quick turnaround, often leading to small datasets in which methods such as data augmentation will be employed to improve the training process of our time-series signals. / Thesis / Master of Applied Science (MASc)

The major contribution of this dissertation is the development of a new integrated measurement and control system for intermixed biogas and photovoltaic systems to achieve safe and optimal energy usage. Literature and field studies show that existing control methods fall short of comprehensive system optimization and fault diagnosis, hence the need to re-look these control methods. The control strategy developed in this dissertation is a considerable enhancement on existing strategies as it incorporates intelligent fuzzy logic algorithms based on C source codes developed on the MPLABX programming environment. Measurements centered on the PIC18F4550 microcontroller were carried out on existing biogas and photovoltaic installations. The designed system was able to accurately predict digester stability, quantify biogas output and carry out biogas fault detection and control. Optimized battery charging and photovoltaic fault detection and control was also successfully implemented. The system optimizes the operation and performance of biogas and photovoltaic energy generation. / Electrical Engineering / M. Tech. (Electrical Engineering)

Ce document synthétise mes travaux de thèse de doctorat en Automatique Productiqueà Grenoble INP (Institut National Polytechnique), thèse préparée au sein dudépartement automatique du laboratoire GIPSA-lab (Grenoble Image Parole Signal etAutomatique). Ce travail s’inscrit dans le cadre du contrôle local et de la supervisiondes systèmes de trafic routier. Les principales contributions portent sur la modélisation,la supervision et la commande locale des systèmes de trafic routier.La contribution apportée à la modélisation du trafic est l’ajout d’un modèle d’incertitudesur le modèle CTM (Cell Transmission Model [Daganzo, 1994]). Ce nouveaumodèle permet de prendre en compte les incertitudes sur différents paramètres dumodèle pour in-fine proposer de nouvelles stratégies de commandes commutées robustes.Outre cette approche de modélisation, nous proposons un niveau de supervisionpermettant d’une part d’estimer en temps réel le mode de fonctionnement et d’autrepart de détecter, localiser et estimer certaines fautes sur le système. L’estimation dynamiquede mode de fonctionnement nous permet de connaître l’état de congestion (ou denon-congestion) de l’aménagement routier considéré. Nous sommes en mesure de détecterdes fautes telles que des chutes de vitesse ou des chutes de capacité survenant sur la route.Enfin, nous proposons deux lois de commandes locales basées sur la théorie dessystèmes à commutations. Ainsi, le schéma de contrôle s’adaptera dynamiquementaux changements de propriétés du système. Ces lois de commande ont pour objet des’insérer dans un schéma de régulation hiérarchique. / This document synthesizes my Phd thesis work in Automatic Control in Grenoble-INP. This thesis has been prepared in the automatic control department of thelaboratory GIPSA-lab. This work is situated in the area of traffic systems control andsupervision. Our contributions are about modeling, supervision and local traffic control.The CTM traffic model has been extended with a model of uncertainties. Thisnews model allows us to take into account the uncertain parameters of the model, topropose new robust switched control law.In addition to this modeling approach, we propose some developments on supervisionof trafic systems. On one hand, we can estimate the operating mode of thesystem in real time and on the other hand to estimate some faults on the system. Thedynamical estimation of the operating mode allows us to know the state of congestion(or non congestion) of the road. We are able to estimate faults such as speed fall andcapacities drop that may appear.Finally, we propose two control laws based on switching systems control. The developedcontrollers adapt their geometry to the properties of the system. The purposeof these controllers is to be inserted in a hierarchic control scheme.

[pt] Calibrações manuais periódicas asseguram o funcionamento correto de um instrumento durante certo período de tempo, mas não garantem que sensores defeituosos permaneçam calibrados por outros períodos, além de eventualmente serem desnecessárias. Em plantas industriais, a análise dos sinais fornecidos pelos sensores que monitoram os processos de produção é uma tarefa difícil em virtude da grande dimensão dos dados. Portanto, uma estratégia de monitoramento e correção online para múltiplos sensores se faz necessária. Este trabalho propõe a utilização de dois modelos: um que emprega Redes Neurais Auto-Associativas com Treinamento Robusto Modificado (RNAAM), e outro que emprega o Teste Seqüencial da Razão de Probabilidade (SPRT) e Lógica Fuzzy. Esses modelos são aplicados em um sistema de monitoramento para auto-correção online e auto-validação das medições realizadas por um grande número de sensores. Diferentemente dos modelos existentes, o sistema proposto faz uso de apenas uma RNAAM para reconstruir os sinais dos sensores com falha. Além disso, os estados do sensor e do modelo de auto-correção são validados por meio dos módulos de confiabilidade e de classificação, respectivamente. Para avaliação do modelo, faz-se uso de uma base de dados com medidas de sensores industriais que controlam e realizam o monitoramento de um motor de combustão interna, instalado em um caminhão de mineração. Os resultados obtidos mostram a capacidade do modelo proposto de mapear e corrigir, com um baixo nível de erro, os sinais dos sensores que apresentam falhas, além de fornecer ao especialista uma ferramenta para a realização de cronogramas de revisão de sensores. / [en] Periodical manual calibrations assure the correct operation of an instrument for a certain period of time, but do not guarantee that faulty sensors remain calibrated for other periods, besides being occasionally unnecessary. In industrial plants the analysis of signals from sensors that monitor a plant is a difficult task due to the high-dimensionality of data. Therefore an online strategy for monitoring and correcting multiple sensors is required. This work proposes the use of two models: one that employs Auto Associative Neural Networks with a Modified Robust Training, and another that employs the Sequential Probability Ratio Test (SPRT) and Fuzzy Logic. These models are applied to an online monitoring system for self-correction and selfvalidation of measurements generated by a large number of sensors. Unlike existing models, the proposed system makes use of only one AANN to reconstruct signals from faulty sensors. Moreover, the states of the sensor and of the self correction model are validated through the reliability and classification modules, respectively. The model is evaluated with a database containing measurements of industrial sensors that control and carry out the monitoring of an internal combustion engine installed in a mining truck. Experimental results show the ability of the proposed model to map and correct signals from faulty sensors with a low error, and to provide a tool for sensor review scheduling.

Rolling element bearings are a vital part in many rotating machinery, including vehicles. A defective bearing can be a symptom of other problems in the machinery and is due to a high failure rate. Early detection of bearing defects can therefore help to prevent malfunction which ultimately could lead to a total collapse. The thesis is done in collaboration with Scania that wants a better understanding of how external sensors such as accelerometers, can be used for condition monitoring in their gearboxes. Defective bearings creates vibrations with specific frequencies, known as Bearing Characteristic Frequencies, BCF [23]. A key component in the proposed method is based on identification and extraction of these frequencies from vibration signals from accelerometers mounted near the monitored bearing. Three solutions are proposed for automatic bearing fault detection. Two are based on data-driven classification using a set of machine learning methods called Support Vector Machines and one method using only the computed characteristic frequencies from the considered bearing faults. Two types of features are developed as inputs to the data-driven classifiers. One is based on the extracted amplitudes of the BCF and the other on statistical properties from Intrinsic Mode Functions generated by an improved Empirical Mode Decomposition algorithm. In order to enhance the diagnostic information in the vibration signals two pre-processing steps are proposed. Separation of the bearing signal from masking noise are done with the Cepstral Editing Procedure, which removes discrete frequencies from the raw vibration signal. Enhancement of the bearing signal is achieved by band pass filtering and amplitude demodulation. The frequency band is produced by the band selection algorithms Kurtogram and Autogram. The proposed methods are evaluated on two large public data sets considering bearing fault classification using accelerometer data, and a smaller data set collected from a Scania gearbox. The produced features achieved significant separation on the public and collected data. Manual detection of the induced defect on the outer race on the bearing from the gearbox was achieved. Due to the small amount of training data the automatic solutions were only tested on the public data sets. Isolation performance of correct bearing and fault mode among multiplebearings were investigated. One of the best trade offs achieved was 76.39 % fault detection rate with 8.33 % false alarm rate. Another was 54.86 % fault detection rate with 0 % false alarm rate.

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.

Orientador: Gilberto Pechoto de Melo / Banca: Amarildo Tabone Paschoalini / Banca: Valder Steffen Júnior / Resumo: Neste trabalho desenvolveram-se metodologias de diagnóstico de falhas utilizando observadores de estado do tipo Filtro de Kalman, nas quais, as entradas para os observadores são identificadas utilizando as funções ortogonais de Fourier, Legendre e Chebyshev. Um tipo de observador denominado Proporcional-Integral é apresentado para a identificação de entradas desconhecidas. Este observador consegue estimar, sob certas condições, as entradas e ou distúrbios presentes no sistema e essas entradas são utilizadas para a diagnose de falha utilizando um observador do tipo Filtro de Kalman. Também é apresentado o desenvolvimento de uma metodologia de identificação de parâmetros bem como das forças de excitação, através das funções ortogonais, utilizando somente a resposta. Apresentam-se resultados obtidos por meio de simulações computacionais e realizados experimentalmente numa bancada de teste pertencente ao laboratório de vibrações mecânicas do Departamento de Engenharia Mecânica de Ilha Solteira. / Abstract: In this work a methodology for fault diagnosis of mechanical systems was developed by using Kalman Filter state observes, in which the input of the observers are identified by using Fourier, Legendre and Chebyshev orthogonal functions. A proportional-integral observer is presented to the unknown input identification. This observer is able to find the unknown inputs of the system and these inputs are used to fault detection purposes by using a Kalman Filter Observer. The methodology for the identification of system parameters and excitation forces by using only the response of the system, through orthogonal functions. The methodology developed is applied to a mechanical structure containing vibrating tables, in the Mechanical Vibrations Laboratory, at Unesp, Ilha Solteira. / Mestre