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Uppsatsen syftade till att undersöka faktorer kring hemtjänstpersonalens kvarvarande i yrket samt personalens arbetsvillkor. Datainsamlingen utfördes genom en kvalitativ intervjustudie med fyra enhetschefer inom både privat och kommunal hemtjänst.  Bidragande orsaker till att personal lämnar yrket framgick som upplevelser av otillräcklighet, bristande påverkans- och utvecklingsmöjligheter samt arbetstidsförläggningen. Faktorer för kvarvarande uppmärksammades som betydelsen av det relationsnära arbetet, kollegialt utbyte, utvecklingsmöjligheter och stöd i arbetet.  Resultatet indikerade på att bristande resurser var skäl till att personal lämnar yrket och betydelsen för personalens kvarvarande har diskuterats utifrån äldreomsorgens kvalité. Äldreomsorgen har likt andra välfärdsverksamheter skyldigheter i att bedrivas på ett sätt som tillgodogör målgruppens behov varför personalens kompetens och erfarenhet blir av stor betydelse. / The study aimed to examine home care workers’ intentions to remain within their profession and working conditions. Data collection was conducted through qualitative interviews with four first-line managers within private and municipal home care. The first-line managers described experiences of inadequacy, lack of influence and development opportunities, and the working hours as reasons for the care workers’ turnover intentions. Factors regarding remaining in the profession were noted as the importance of relational work, collegiate exchange, development opportunities, and support. The results indicated lack of resources as the main reason for home care workers leaving their profession, and the importance of remaining has been discussed based on the quality of elderly care. Elderly care has obligations to be conducted in a manner that meets the needs of the targeted group, which is why the home care workers’ competence and experience are of significant importance.

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This dissertation is focused on understanding the performance of a particular fiber-reinforced polymeric composite structural beam, a 91.4 cm (36 inch) deep pultruded double-web beam (DWB) designed for bridge construction. Part 1 focuses on calculating the Timoshenko shear stiffness of the DWB and understanding what factors may introduce error in the experimental measurement of the quantity for this and other sections. Laminated beam theory and finite element analysis (FEA) were used to estimate the shear stiffness. Several references in the literature have hypothesized an increase in the effective measured shear stiffness due to warping. A third order laminated beam theory (TLBT) was derived to explore this concept, and the warping effect was found to be negligible. Furthermore, FEA results actually indicate a decrease in the effective shear stiffness at shorter spans for simple boundary conditions. This effect was attributed to transverse compression at the load points and supports. The higher order sandwich theory of Frostig shows promise for estimating the compression related error in the shear stiffness for thin-walled beams. Part 2 attempts to identify the failure mechanism(s) under quasi-static loading and to develop a strength prediction for the DWB. FEA was utilized to investigate two possible failure modes in the top flange: compression failure of the carbon fiber plies and delamination at the free edges or taper regions. The onset of delamination was predicted using a strength-based approach, and the stress analysis was accomplished using a successive sub-modeling approach in ANSYS. The results of the delamination analyses were inconclusive, but the predicted strengths based on the compression failure mode show excellent agreement with the experimental data. A fatigue life prediction, assuming compression failure, was also developed using the remaining strength and critical element concepts of Reifsnider et al. One DWB fatigued at about 30% of the ultimate capacity showed no signs of damage after 4.9 million cycles, although the predicted number of cycles to failure was 4.4 million. A test on a second beam at about 60% of the ultimate capacity was incomplete at the time of publication. Thus, the success of the fatigue life prediction was not confirmed. / Ph. D.

To achieve satisfactory levels of strength, fracture toughness, and reliability for man-rated systems such as jet engines, fiber reinforced ceramic matrix composites are needed. An elevated temperature axial testing system is developed to investigate and characterize fatigue behavior of Nicalon fiber reinforced enhanced silicon carbide matrix. composites at 1800 of under fully reversed cyclic loading. Notch effect on quasi-static tensile response is also considered. Quasi-static and fatigue damage mechanisms and failure modes are examined using various specimen geometries, load levels, fatigue ratios, and laminates stacking sequences by employing a number of NDE techniques. Issues such as damage tolerance and durability are addressed by conducting interrupted fatigue tests at various stages of life for different load levels. Results are compared to the predictions of remaining strength and life, obtained using a performance simulation code. Initial results indicate existence of a threshold stress value which limits the use of the material system. / Ph. D.

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. / Big data have been providing both opportunities and challenges for product quality assurance and improvement in modern manufacturing systems. In aerospace industry, broaching processes are one of the most important manufacturing processes as they are used to produce the turbine discs in the jet engine. Nonconforming turbine disc quality, either in terms of compromised surface finish or geometry accuracy, will lead to malfunction or even catastrophic failures in the aircraft engines. One of the major sources that lead to nonconforming product quality is excessive tool wear accumulation and other abrupt malfunctions of the broaching tools. In broaching processes, multiple cutting edges are sequentially pushed or pulled through the workpiece, and each cutting edge is responsible to shape the workpiece into a specific intermediate shaped contour. Therefore, a broaching process can be regarded as a multistage manufacturing process with variation propagating through the multiple cutting edges. The overarching goal of this dissertation is to explore how process knowledge can be used to improve process monitoring and prognostics for a complex manufacturing process like broaching. This dissertation focuses on the quality assurance and improvement for broaching processes which includes: 1) timely abrupt process fault detection; 2) tool performance degradation quantification; and 3) remaining tool life prediction, which contributes to both methodological development and practical applications in advanced sensing analytics in manufacturing systems.

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.

Este trabalho apresenta um procedimento para auxiliar na determinação da vida útil de pavimentos flexíveis através da determinação de tensões e deformações causadas pela solicitação de um eixo padrão na estrutura de pavimentos flexíveis utilizando Redes Neurais Artificiais. Para treinamento e validação das redes foram utilizadas bacias de deflexões hipotéticas geradas com o auxílio do programa ELSYM5, simulando o carregamento com falling weight deflectometer. Foram criados quatro conjuntos de bacias hipotéticas, dois para pavimentos de três camadas e dois para pavimentos de quatro camadas. As redes neurais artificiais foram treinadas e validadas utilizando-se o simulador EasyNN-plus, que utiliza redes multilayer perceptron com algoritmo de aprendizagem backpropagation. Os dados de entrada das redes são as espessuras das camadas do pavimento e a bacia de deflexão. Como saída, têm-se as tensões e deformações na face inferior do revestimento e no topo do subleito e os módulos de resiliência das camadas do pavimento. Foram determinadas retas de regressão, coeficientes de regressão e histogramas de erros entre os valores reais (ELSYM5) e os valores previstos (RNA). Os resultados obtidos pelas redes neurais artificiais apresentaram boa correlação com os valores reais, demonstrando a capacidade das redes neurais para auxiliar na determinação da vida útil de pavimentos flexíveis, ao estimar diretamente as tensões e deformações em pontos específicos da estrutura. / This paper presents a procedure to assist the evaluation of the remaining life of flexible pavements by means of the determination of stresses and strains caused by a standard load in flexible pavements structures using artificial neural networks. Hypothetical deflections basins, generated by the ELSYM5 program, simulating the load applied by a falling weight deflectometer, were used to train and to validate the networks. Four sets of hypothetical basins were created, two for pavements with three layers and two for pavements with four layers. The artificial neural networks were trained and validated using the EasyNN-plus simulator, which uses multilayer perceptron networks with back-propagation learning algorithm. The networks input data are the pavements layers thickness and the deflection basin. The networks outputs are the stresses and strains in the bottom of the asphalt layer and at the top of the subgrade and resilience modulus of the pavement layers. The results obtained by the artificial neural networks showed good correlation with the real values, demonstrating that neural networks have capacity to assist in the evaluation of the remaining life of flexible pavements, estimating directly the stresses and strains of specific points of the pavement structure.