Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-06-26T22:26:10Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) / Made available in DSpace on 2018-06-26T22:26:10Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) Previous issue date: 2017-02-21 / CAPES / The useful life time of equipment is an important variable related to reliability and maintenance. The knowledge about the useful remaining life of operation system by means of a prognostic and health monitoring could lead to competitive advantage to the corporations. There are numbers of models trying to predict the reliability’s variable behavior, such as the remaining useful life, from different types of signal (e.g. vibration signal), however several could not be realistic due to the imposed simplifications. An alternative to those models are the learning methods, used when exist many observations about the variable. A well-known method is Support Vector Machine (SVM), with the advantage that is not necessary previous knowledge about neither the function’s behavior nor the relation between input and output. In order to achieve the best SVM’s parameters, a Particle Swarm Optimization (PSO) algorithm is coupled to enhance the solution. Empirical Mode Decomposition (EMD) and Wavelets rise as two preprocessing methods seeking to improve the input data analysis. In this paper, EMD and wavelets are used coupled with PSO+SVM to predict the rolling bearing Remaining Useful Life (RUL) from a vibration signal and compare with the prediction without any preprocessing technique. As conclusion, EMD models presented accurate predictions and outperformed the other models tested. / O tempo de vida útil de um equipamento é uma importante variável relacionada à confiabilidade e à manutenção, e o conhecimento sobre o tempo útil remanescente de um sistema em operação, por meio de um monitoramento do prognóstico de saúde, pode gerar vantagens competitivas para as corporações. Existem diversos modelos utilizados na tentativa de prever o comportamento de variáveis de confiabilidade, tal como a vida útil remanescente, a partir de diferentes tipos de sinais (e.g. sinal de vibração), porém alguns podem não ser realistas, devido às simplificações impostas. Uma alternativa a esses modelos são os métodos de aprendizado, utilizados quando se dispõe de diversas observações da variável. Um conhecido método de aprendizado supervisionado é o Support Vector Machine (SVM), que gera um mapeamento de funções de entrada-saída a partir de um conjunto de treinamento. Para encontrar os melhores parâmetros do SVM, o algoritmo de Particle Swarm Optimization (PSO) é acoplado para melhorar a solução. Empirical Mode Decomposition (EMD) e Wavelets são usados como métodos pré-processamento que buscam melhorar a qualidade dos dados de entrada para PSO+SVM. Neste trabalho, EMD e Wavelets foram usadas juntamente com PSO+SVM para estimar o tempo de vida útil remanescente de rolamentos a partir de sinais de vibração. Os resultados obtidos com e sem as técnicas de pré-processamento foram comparados. Ao final, é mostrado que modelos baseados em EMD apresentaram boa acurácia e superaram o desempenho dos outros modelos testados.

Turbines encounter blade failures due to fatigue and creep. It has been shown in the literature that the primary cause of steam turbine blade failures worldwide can be ascribed to fatigue in low pressure (LP) turbine blades. The failure and damage to these blades can lead to catastrophic consequences. Some utilities use empirical methods to determine the forces experienced by turbine blades but desire more accurate methods. The inaccurate prediction of high-cycle fatigue (HCF), thermal durability and stage performance is introduced when one does not consider blade row interaction. Blade row interactions can, however, be accounted for by means of computational fluid dynamics (CFD). Furthermore, modern high- fidelity CFD tools would be able to contribute greatly in predicting the forces experienced by turbine blades. Numerical tools such as CFD and nite element analysis (FEA) can greatly contribute to the estimation of the remaining useful life (RUL) of turbine blades. However, in this estimation process, there are various uncertainties and aspects that affect the estimated RUL. Understanding the sensitivity of the estimated RUL to these various uncertainties and aspects is of great importance if RUL is to be estimated as accurately as possible. In this dissertation, a sensitivity analysis is performed with the purpose of establishing the sensitivity of the estimated RUL of the last stage rotor of an LP steam turbine, to the number of harmonics used in a nonlinear harmonic (NLH) CFD simulation. The sensitivity of the estimated RUL is evaluated in the HCF regime, where the cyclic stresses occur below the yield strength of the turbine blade. A CFD model, FE model, and fatigue model were therefore developed in such a manner that would suffice, regarding the purpose of the sensitivity analysis. The CFD model is validated by comparing the predicted CFD power to that of actual generated power of a dual 100MW LP steam turbine. The sensitivity analysis is performed for 3 operation conditions, and for each operational condition the aerodynamic forces were computed using 1, 2, and 3 harmonics in an NLH simulation. The estimation process considers a weak coupling between the CFD model and FE model. NLH simulations are firstly performed to calculate the unsteady static surface pressure distributions on the last stage rotor. This is followed by the mapping thereof to the FE model, for which a transient structural analysis is performed. Finally, the RUL is estimated by performing a fatigue analysis on the stress history obtained from the transient structural analysis. Based on the results of the sensitivity analysis, the following recommendations were made, from a conservative point of view. Firstly, in general, if the RUL is to be estimated with reasonable accuracy, just using 1 harmonic in an NLH simulation will not be sufficient and 2 harmonics should be used. Secondly, if the RUL has to be estimated with high accuracy, 3 harmonics should be used. / Dissertation (MEng)--University of Pretoria, 2018. / National Research Foundation (NRF) / Mechanical and Aeronautical Engineering / MEng / Unrestricted

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With the advancement of sensor technology and data processing capacities, various types of high volume data are available for process monitoring and prognostics in manufacturing systems. In a broaching process, a multi-toothed broaching tool removes material from the workpiece by sequential engagement and disengagement of multiple cutting edges. The quality of the final part, including the geometric integrity and surface finish, is highly dependent upon the broaching tool condition. Though there has been a considerable amount of research on tool condition monitoring and prognostics for various machining processes, the broaching process is unique in the following aspects: 1) a broaching process involves multiple cutting edges, which jointly contribute to the final part quality; 2) the resharpening and any other process adjustments to the tool can only be performed with the whole broaching tool or at least a whole segment of the tool replaced. The overarching goal of this research is to explore how engineering knowledge can be used to improve process monitoring and prognostics for a complex manufacturing process like broaching. This dissertation addresses the needs for developing new monitoring and prognostics approaches based on various types of data. Specifically, the research effort focuses on 1) the use of in-situ force profile data for real-time process monitoring and fault diagnosis, 2) degradation characterization for broaching processes on an individual component level based on image processing; and 3) system-level degradation modeling and remaining useful life prediction for broaching processes based on multiple images. / Ph. D.

La disponibilité élevée des systèmes technologiques comme l'aérospatial, la défense, la pétrochimie et l'automobile, est un but important des nouveaux développements de la technologie de conception des systèmes sachant que la défaillance onéreuse survient, en général, soudainement. Afin de rendre les stratégies classiques de maintenance plus efficaces et pour prendre en considération l'état et l'environnement évolutifs du produit, un nouveau modèle de pronostic analytique est développé en tant que complément des stratégies de maintenance existantes. Ce nouveau modèle est appliqué aux systèmes mécaniques soumis à la défaillance par fatigue sous charge cyclique répétitive. Sachant que l'effet de fatigue va initier des microfissures qui peuvent se propager soudainement et conduire à la défaillance. Ce modèle est basé sur des lois d'endommagement existantes dans la mécanique de la rupture comme la loi de propagation de fissures de Paris-Erdogan à côté de la loi de cumul de dommage de Palmgren-Miner. A partir d'un seuil prédéfini de dégradation DC, la durée de vie résiduelle (RUL) est estimée à l'aide de ce modèle de pronostic. Les dommages peuvent être cumulés linéairement (Loi de Palmgren-Miner) et aussi non linéairement afin de prendre en compte un comportement plus complexe des chargements et des matériaux. Le modèle de dégradation développé dans ce travail est basé sur une sommation d'une mesure de dommage D à la suite de chaque cycle de chargement. Quand cette mesure devient égale à un seuil prédéfini DC, le système est considéré dans l'état de panne. En plus, l'influence stochastique est incluse dans notre modèle pour le rendre plus précis et réaliste. / The high availability of technological systems like aerospace, defense, petro-chemistry and automobile, is an important goal of earlier recent developments in system design technology knowing that the expensive failure can generally occur suddenly. To make the classical strategies of maintenance more efficient and to take into account the evolving product state and environment, a new analytic prognostic model is developed as a complement of existent maintenance strategies. This new model is applied to mechanical systems that are subject to fatigue failure under repetitive cyclic loading. Knowing that, the fatigue effects will initiate micro-cracks that can propagate suddenly and lead to failure. This model is based on existing damage laws in fracture mechanics, such as the crack propagation law of Paris-Erdogan beside the damage accumulation law of Palmgren-Miner. From a predefined threshold of degradation DC, the Remaining Useful Lifetime (RUL) is estimated by this prognostic model. Damages can be assumed to be accumulated linearly (Palmgren-Miner's law) and also nonlinearly to take into consideration the more complex behavior of loading and materials. The degradation model developed in this work is based on the accumulation of a damage measurement D after each loading cycle. When this measure reaches the predefined threshold DC, the system is considered in wear out state. Furthermore, the stochastic influence is included to make the model more accurate and realistic.

La mise en place d’une politique de maintenance prévisionnelle est un défi majeur dans l’industrie qui tente de réduire le plus possible les frais relatifs à la maintenance. En effet, les systèmes sont de plus en plus complexes et demandent un suivi de plus en plus poussé afin de rester opérationnels et sécurisés. Une maintenance prévisionnelle nécessite d’une part d’évaluer l’état de dégradation des composants du système, et d’autre part de pronostiquer l’apparition future d’une panne. Plus précisément, il s’agit d’estimer le temps restant avant l’arrivée d’une défaillance, aussi appelé Remaining Useful Life ou RUL en anglais. L’estimation d’une RUL constitue un réel enjeu car la pertinence et l’efficacité des actions de maintenance dépendent de la justesse et de la précision des résultats obtenus. Il existe de nombreuses méthodes permettant de réaliser un pronostic de durée de vie résiduelle, chacune avec ses spécificités, ses avantages et ses inconvénients. Les travaux présentés dans ce manuscrit s’intéressent à une méthodologie générale pour estimer la RUL d’un composant. L’objectif est de proposer une méthode applicable à un grand nombre de cas et de situations différentes sans nécessiter de modification majeure. De plus, nous cherchons aussi à traiter plusieurs types d’incertitudes afin d’améliorer la justesse des résultats de pronostic. Au final, la méthodologie développée constitue une aide à la décision pour la planification des opérations de maintenance. La RUL estimée permet de décider de l’instant optimal des interventions nécessaires, et le traitement des incertitudes apporte un niveau de confiance supplémentaire dans les valeurs obtenues. / Predictive maintenance strategies can help reduce the ever-growing maintenance costs, but their implementation represents a major challenge. Indeed, it requires to evaluate the health state of the component of the system and to prognosticate the occurrence of a future failure. This second step consists in estimating the remaining useful life (RUL) of the components, in Other words, the time they will continue functioning properly. This RUL estimation holds a high stake because the precision and accuracy of the results will influence the relevance and effectiveness of the maintenance operations. Many methods have been developed to prognosticate the remaining useful life of a component. Each one has its own particularities, advantages and drawbacks. The present work proposes a general methodology for component RUL estimation. The objective i to develop a method that can be applied to many different cases and situations and does not require big modifications. Moreover, several types of uncertainties are being dealt With in order to improve the accuracy of the prognostic. The proposed methodology can help in the maintenance decision making process. Indeed, it is possible to select the optimal moment for a required intervention thanks to the estimated RUL. Furthermore, dealing With the uncertainties provides additional confidence into the prognostic results.

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches. Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place.

Tato práce se zabývá vytvořením simulačního modelu dvojčinného pneumatického pístu s mechanickou sestavou, včetně modelů snímačů, s následujícím odhadem parametrů a aproximací chování demonstračního zařízení. Dalším cílem je prezentace různých přístupů prediktivní údržby na datové sadě měřené na demonstračním zařízení. Na měřený datový soubor se aplikovaly signal-based techniky bez použití simulačního modelu a model-based metody, které vyžadují použití simulačního modelu. Výsledkem této práce je ověření možnosti monitorování stavu zařízení pomocí nainstalovaných senzorů a vyhodnocení efektivity senzorů z hlediska přesnosti a finančních nákladů.