Factory shop floors of the future will see a significant increase in interconnected devices for monitoring and control. However, if a Service Orientated Architecture (SOA) is implemented on all such devices then this will result in a large number of permutations of services and composite services. These services combined with other business level components can pose a huge challenge to manage as it is often difficult to keep an overview of all the devices, equipment and services. This thesis proposes an SOA based novel assimilation architecture for integrating disparate industrial hardware based processes and business processes of an enterprise in particular the plastics machinery environment. The key benefits of the proposed architecture are the reduction of complexity when integrating disparate hardware platforms; managing the associated services as well as allowing the Micro Injection Moulding (µIM) process to be monitored on the web through service and data integration. An Enterprise Service Bus (ESB) based middleware layer integrates the Wireless Sensor Network (WSN) based environmental and simulated machine process systems with frontend Google Gadgets (GGs) based web visualisation applications. A business process framework is proposed to manage and orchestrate the resulting services from the architecture. Results from the analysis of the WSN kits in terms of their usability and reliability showed that the Jennic WSN was easy to setup and had a reliable communication link in the polymer industrial environment with the PER being below 0.5%. The prototype Jennic WSN based µIM process monitoring system had limitations when monitoring high-resolution machine data, therefore a novel hybrid integration architecture was proposed. The assimilation architecture was implemented on a distributed server based test bed. Results from test scenarios showed that the architecture was highly scalable and could potentially allow a large number of disparate sensor based hardware systems and services to be hosted, managed, visualised and linked to form a cohesive business process.

Care process monitoring is important in healthcare domains to provide precise and detailed analytics on patients, providers, and resources participating in a care process and their status. These analytics are used to keep track of whether the quality of care goals set by healthcare organizations are satisfied and ensure that legislative and organizational guidelines are followed. The complexity of care process monitoring can vary depending on whether the care process takes place in a hospital or out in the community, and it can vary depending on the complexity of the information technology infrastructure that is in place to support the care process. A Care Process Monitoring Application (CPMA) is a software application which collects and integrates data from various sources while a care process is being provided, in order to provide performance reporting of metrics that are used to measure how well the performance goals and guidelines for the care process are being met. In our research, we have studied how CPMAs are built in order to improve the quality of their engineering. The significant challenge in this context is how to engineer a CPMA so that the engineering process is repeatable, produces a CPMA of consistent high quality, and requires less time, less effort and less complexity. This thesis proposes an application framework for care process monitoring that collects and integrates events from event sources, maintains the individual and aggregate states of the care process and populates a metrics data mart to support performance reporting. Our contributions are the following: a state-based application meta-model of care process monitoring, a care process monitoring architectural pattern, and finally, a behavior driven development methodology for CPMAs based on our meta-model and architectural pattern. Our results are validated through three different case studies in which we collaborated with two different health care organizations to build and deploy CPMAs for two different care processes (one hospital-based, the other community-based) in collaboration with healthcare clinicians and researchers.

Cette étude porte sur la mise en œuvre par infusion d'une nouvelle génération de renfort spécialement dédiée aux procédés LCM (Liquid Composite Molding) pour la réalisation de pièces composites. Par l’expérimentation et la simulation numérique, l’objectif de ces travaux est de comprendre et maîtriser les paramètres de fabrication afin de définir des outils de modélisation et de simulation représentatifs voir prédictifs du procédé. La caractérisation des paramètres du procédé et de la santé matière finale des pièces ont ainsi permis de définir l'échelle de modélisation et les phénomènes physiques devant être simulés. La stratégie numérique adoptée repose sur un couplage fort entre les équations de Stokes et de Darcy dans un cadre de grandes transformations et où les interfaces sont représentées par des Level-Set. Ce couplage modélise l'écoulement dans la préforme fibreuse assimilée à un milieu poreux homogène équivalent (modèle de Darcy) et l'écoulement dans les drainants (modèle de Stokes). Un modèle de Terzaghi réalise le couplage fluide-solide en représentant l'action du fluide sur le renfort via sa pression hydrostatique. L’évolution du taux volumique de fibres et de la perméabilité du milieu sont ainsi actualisés dans les différents problèmes. La confrontation des simulations numériques aux caractérisations expérimentales mettent en évidence qu'un effort important doit être réalisé dans la compréhension, la définition et la caractérisation de la perméabilité. Sur les renforts de l'étude, la question même de la représentation et de l'homogénéisation d'écoulements locaux complexes à travers la seule notion de perméabilité est à poser. / This study deals with the manufacturing by infusion of a new high performance fibrous reinforcement solution developed for LCM (Liquid Composite Molding) composite materials manufacturing processes. Combining simulations with experimentations, the aim of this work is to understand and control the process parameters in order to develop accurate simulation tools. Involving specific experimental plan and protocols combined with in-situ monitoring technologies this study points out the physics and length scales challenging the process simulation. The numerical strategy considered in this works is based on a strong coupling between a Stokes and Darcy model undergoing large strains and where interfaces are represented and managed by a Level-Set method. At the process scale, the fluid mechanics problem describes the resin flow through the distribution medium and then through fibrous preforms assimilated to porous medium. A key feature of our approach is the fluid-solid interaction leading to couple a fluid/porous flow with a non-linear solid mechanic problem. The interaction phenomenon due to the resin flow in the orthotropic highly compressible preform is based on both Terzaghi’s law and explicit relations expressing permeability as function of porosity. Some numerical simulations are presented and compared to the experimental characterizations. The results point out the the first necessity to work on the permeability comprehension, definition and characterization. Applied to the specific reinforcements of the study, the representation of the complex local flows and their homogenization through this single permeability notion can also be questionable.

O desafio para a área de monitoramento de processos, em indústrias químicas, ainda é a etapa de detecção, com a necessidade de desenvolvimento de sistemas confiáveis. Pode-se resumir que um sistema é confiável, ao ser capaz de detectar as situações anormais, de modo precoce, e, ao mesmo tempo, de minimizar a geração de alarmes falsos. Ao se ter um sistema confiável, pode-se empregá-lo para auxiliar o operador, de fábricas, no processo de tomada de decisões. O objetivo deste estudo é apresentar uma metodologia, baseada na técnica, modelo oculto de Markov (HMM, acrônimo de ?Hidden Markov Model?), para se detectar situações anormais em caldeiras de recuperação química. As aplicações de maior sucesso de HMM são na área de reconhecimento de fala. Pode-se citar como aspectos positivos: o raciocínio probabilístico, a modelagem explícita, e a identificação a partir de dados históricos. Fez-se duas aplicações. O primeiro estudo de caso é no ?benchmark? de um sistema de evaporação múltiplo efeito de uma fábrica de produção de açúcar. Identificou-se um HMM, característico de operação normal, para se detectar cinco situações anormais no atuador responsável por regular o fluxo de xarope de açúcar para o primeiro evaporador. A detecção, para as três situações abruptas, é imediata, uma vez que o HMM foi capaz de detectar alterações, abruptas, no sinal da variável monitorada. Em relação às duas situações incipientes, foi possível detectá-las ainda em estágio inicial; ao ser o valor de f (vetor responsável por representar a intensidade de um evento anormal, com o tempo), no instante da detecção, próximo a zero, igual a 2,8% e 2,1%, respectivamente. O segundo estudo de caso é em uma caldeira de recuperação química, de uma fábrica de produção de celulose, no Brasil. O objetivo é monitorar o acúmulo de depósitos de cinzas sobre os equipamentos da sessão de transferência de calor convectivo, através de medições de perda de carga. Este é um dos principais desafios para se aumentar a eficiência operacional deste equipamento. Após a identificação de um HMM característico de perda de carga alta, pôde-se verificar a sua capacidade de informar o estado atual e, por consequência, a tendência do sistema, de modo similar à um preditor. Pôde-se demonstrar também a utilidade de se definir limites de controle, com o objetivo de se ter a informação sobre a distância entre o estado atual e os níveis de alarme de perda de carga. / The greatest challenge faced by the area of process monitoring in chemical industries still resides in the fault detection task, which aims at developing reliable systems. One may say that a system is reliable if it is able to perform early fault detection and, at the same time, to reduce the generation of false alarms. Once there is a reliable system available, it can be employed to help operators, in factories, in the decisionmaking process. The aim of this study is presenting a methodology, based on the Hidden Markov Model (HMM) technique, suggesting its use in the detection of abnormal situations in chemical recovery boilers. The most successful applications of HMM are in the area of speech recognition. Some of its advantages are: probabilistic reasoning, explicit modeling and the identification based on process history data. This study discusses two applications. The first one is on a benchmark of a multiple evaporation system in a sugar factory. A HMM representative of the normal operation was identified, in order to detect five abnormal situations at the actuator responsible for controlling the syrup flow to the first evaporator. The detection result for the three abrupt situations was immediate, since the HMM was capable of detecting the statistical changes on the signal of the monitored variable as soon as they occurred. Regarding to the two incipient situations, the detection was done at an early stage. For both events, the value of vector f (responsible for representing the strength of an abnormal event over time), at the time it occurred, was near zero, equal to 2.8 and 2.1%, respectively. The second case study deals with the application of HMM in a chemical recovery boiler, belonging to a cellulose mill, in Brazil. The aim is monitoring the accumulation of ash deposits over the equipments of the convective heat transfer section, through pressure drop measures. This is one of the main challenges to be overcome nowadays, bearing in mind the interest that exists in increasing the operational efficiency of this equipment. Initially, a HMM for high values of pressure drop was identified. With this model, it was possible to check its capacity to inform the current state, and consequently, the tendency of the system (similarly as a predictor). It was also possible to show the utility of defining control limits, in order to inform the operator the relative distance between the current state of the system and the alarm levels of pressure drop.

The exponential development of biotechnology has lead to a quasi unlimited number of potential products going from biopolymers to vaccines. Cell culture has therefore evolved from the simple cell growth outside its natural environment to its use to produce molecules that they do not naturally produce. This rapid development could not be continued without new control and supervising tools as well as a good process understanding. This requirement involves however a large diversity and a better accessibility of process measurements. In this framework, software sensors show numerous potentialities. The objective of a software sensor is indeed to provide an estimation of the system state variables and particularly those which are not obtained through in situ hardware sensors or laborious and expensive analysis. In this context, This work attempts to join the knowledge of increasing bioprocess complexity and diversity and the time scale of process developments and favours systematic modelling methodology, its flexibility and the speed of development. In the field of state observation, an important modelling constraint is the one induced by the selection of the state to estimate and the available measurements. Another important constraint is the model quality. The central axe of this work is to provide solutions in order to reduce the weight of these constraints to software sensors development. On this purpose, we propose four solutions to four main questions that may arise. The first two ones concern modelling uncertainties.<p><p>1."How to develop a software sensor using measurements easily available on pilot scale bioreactor?" The proposed solution is a static software sensor using an artificial neural network. Following this modelling methodology we developed static software sensors for the biomass and ethanol concentrations in a pilot scale S. cerevisae cell culture using the measurement of titrating base quantity, agitation rate and CO& / Doctorat en sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished

Care process monitoring is important in healthcare domains to provide precise and detailed analytics on patients, providers, and resources participating in a care process and their status. These analytics are used to keep track of whether the quality of care goals set by healthcare organizations are satisfied and ensure that legislative and organizational guidelines are followed. The complexity of care process monitoring can vary depending on whether the care process takes place in a hospital or out in the community, and it can vary depending on the complexity of the information technology infrastructure that is in place to support the care process. A Care Process Monitoring Application (CPMA) is a software application which collects and integrates data from various sources while a care process is being provided, in order to provide performance reporting of metrics that are used to measure how well the performance goals and guidelines for the care process are being met. In our research, we have studied how CPMAs are built in order to improve the quality of their engineering. The significant challenge in this context is how to engineer a CPMA so that the engineering process is repeatable, produces a CPMA of consistent high quality, and requires less time, less effort and less complexity. This thesis proposes an application framework for care process monitoring that collects and integrates events from event sources, maintains the individual and aggregate states of the care process and populates a metrics data mart to support performance reporting. Our contributions are the following: a state-based application meta-model of care process monitoring, a care process monitoring architectural pattern, and finally, a behavior driven development methodology for CPMAs based on our meta-model and architectural pattern. Our results are validated through three different case studies in which we collaborated with two different health care organizations to build and deploy CPMAs for two different care processes (one hospital-based, the other community-based) in collaboration with healthcare clinicians and researchers.

Brennstoffzellen werden durch Stapelung präziser Bipolarplatten mit komplexer Struktur von Fließkanälen für Flüssigkeiten und Leitungen für den Transfer von Gasen hergestellt. Die für derartige Platten notwendigen Bleche werden durch Stanzen und Umformen erzeugt. Dabei haben neben der Einrichtung von Presse und Werkzeug (z. B. Hubzahl, Stößelverstellung) auch deren Zustand Einfluss auf die Qualität der Platten (Ebenheit, Materialspannungen/Rissbildungen, Gratbildung). Obwohl moderne Pressen heute im Wesentlichen auf servoelektrischen Antrieben basieren, erfolgt die Kraftübertragung auf den Stößel vornehmlich über mehrere Antriebsdruckpunkte. Spezifische Schneide- und Umformwerkzeuge erzeugen auf einer entsprechenden Presse bei definierten Presseneinstellungen und Materialparametern spezifische Kraft-, Moment- und Kippungsverläufe. Die am IWU entwickelte Überwachungslösung ist in der Lage, die genannten Größen hubabhängig zu erfassen. Mittels eines im IO-Zustand von Presse und Werkzeug generierten „Fingerprints“ werden im laufenden Prozess gemessene Abweichungen erkannt und in Echtzeit analysiert. Dies ist insbesondere beim Einsatz dünnster Bleche von enormer Bedeutung, da hier bereits kleinste anlagenbedingte Änderungen zu kostenintensiven Ausfällen an Werkzeug und Produkt führen können.

Double sampling procedure is adapted from a statistical branch called acceptance sampling. The first Shewhart-type double sampling monitoring scheme was introduced in the statistical process monitoring (SPM) field in 1974. The double sampling monitoring scheme has been proven to effectively decrease the sampling effort and, at the same time, to decrease the time to detect potential out-of-control situations when monitoring the location, variability, joint location and variability using univariate or multivariate techniques. Consequently, an overview is conducted to give a full account of all 76 publications on double sampling monitoring schemes that exist in the SPM literature. Moreover, in the review conducted here, these are categorized and summarized so that any research gaps in the SPM literature can easily be identified. Next, based on the knowledge gained from the literature review about the existing designs for monitoring the process mean, a new type of double sampling design is proposed. The new charting region design lead to a class of a control charts called a side-sensitive double sampling (SSDS) monitoring schemes. In this study, the SSDS scheme is implemented to monitor the process mean when the underlying process parameters are known as well as when they are unknown. A variety of run-length properties (i.e., the 5th, 25th, 50th, 75th, 95th percentiles, the average run-length (𝐴𝑅𝐿), standard deviation of the run-length (𝑆𝐷𝑅𝐿), the average sample size (𝐴𝑆𝑆) and the average extra quadratic loss (𝐴𝐸𝑄𝐿) metrics) are used to design and implement the new SSDS scheme. Comparisons with other established monitoring schemes (when parameters are known and unknown) indicate that the proposed SSDS scheme has a better overall performance. Illustrative examples are also given to facilitate the real-life implementation of the proposed SSDS schemes. Finally, a list of possible future research ideas is given with hope that this will stimulate more future research on simple as well as complex double sampling schemes (especially using the newly proposed SSDS design) for monitoring a variety of quality characteristics in the future. / Statistics / M. Sc. (Statistics)

Temperature drifts, tool deterioration, unknown vibrations as well as spindle play are major effects which decrease the achievable precision of computerized numerically controlled (CNC) lathes and lead to shape deviations between the processed work pieces. Since currently no measurement system exist for fast, precise and insitu 3d shape monitoring with keyhole access, much effort has to be made to simulate and compensate these effects. Therefore we introduce an optical interferometric sensor for absolute 3d shape measurements, which was integrated into a working lathe. According to the spindle rotational speed, a measurement rate of 2,500 Hz was achieved. In-situ absolute shape, surface profile and vibration measurements are presented. While thermal drifts of the sensor led to errors of several µm for the absolute shape, reference measurements with a coordinate machine show, that the surface profile could be measured with an uncertainty below one micron. Additionally, the spindle play of 0.8 µm was measured with the sensor.

The effect of a controlled oxygen admixture to a plasma nitrocarburizing process using active screen technology and an active screen made of carbon was investigated to control the carburizing potential within the plasma-assisted process. Laser absorption spectroscopy was used to determine the resulting process gas composition at different levels of oxygen admixture using O2 and CO2, respectively, as well as the long-term trends of the concentration of major reaction products over the duration of a material treatment of ARMCO® iron. The short-term studies of the resulting process gas composition, as a function of oxygen addition to the process feed gases N2 and H2, showed that a stepwise increase in oxygen addition led to the formation of oxygen-containing species, such as CO, CO2, and H2O, and to a significant decrease in the concentrations of hydrocarbons and HCN. Despite increased oxygen concentration within the process gas, no oxygen enrichment was observed in the compound layer of ARMCO® iron; however, the diffusion depth of nitrogen and carbon increased significantly. Increasing the local nitrogen concentration changed the stoichiometry of the ε-Fe3(N,C)1+x phase in the compound layer and opens up additional degrees of freedom for improved process control.