Made available in DSpace on 2014-12-17T14:55:21Z (GMT). No. of bitstreams: 1 MarceloRBGV_TESE.pdf: 4018928 bytes, checksum: 7940c018115fd94c74a5dbbd7d3f7fb6 (MD5) Previous issue date: 2014-06-27 / The industries are getting more and more rigorous, when security is in question, no matter is to avoid financial damages due to accidents and low productivity, or when it s related to the environment protection. It was thinking about great world accidents around the world involving aircrafts and industrial process (nuclear, petrochemical and so on) that we decided to invest in systems that could detect fault and diagnosis (FDD) them. The FDD systems can avoid eventual fault helping man on the maintenance and exchange of defective equipments. Nowadays, the issues that involve detection, isolation, diagnose and the controlling of tolerance fault are gathering strength in the academic and industrial environment. It is based on this fact, in this work, we discuss the importance of techniques that can assist in the development of systems for Fault Detection and Diagnosis (FDD) and propose a hybrid method for FDD in dynamic systems. We present a brief history to contextualize the techniques used in working environments. The detection of fault in the proposed system is based on state observers in conjunction with other statistical techniques. The principal idea is to use the observer himself, in addition to serving as an analytical redundancy, in allowing the creation of a residue. This residue is used in FDD. A signature database assists in the identification of system faults, which based on the signatures derived from trend analysis of the residue signal and its difference, performs the classification of the faults based purely on a decision tree. This FDD system is tested and validated in two plants: a simulated plant with coupled tanks and didactic plant with industrial instrumentation. All collected results of those tests will be discussed / As ind?strias est?o cada vez mais rigorosas quando o assunto ? seguran?a, tanto para evitar preju?zos financeiros com acidentes e baixa produtividade, quanto para preservar o meio ambiente. Diante dos grandes acidentes em todo o mundo envolvendo aeronaves e processos industriais (nucleares, petroqu?micos etc) procurou-se investir em sistemas que pudessem detectar e diagnosticar falhas (FDD-Fault Detection and Diagnosis). Os sistemas FDD podem evitar eventuais falhas auxiliando o homem na manuten??o e troca de equipamentos defeituosos. Nos dias de hoje os assuntos que envolvem detec??o, isolamento, identifica??o e diagn?stico de falhas est?o ganhando for?a no meio acad?mico e industrial. Diante deste impulso, neste trabalho ser? discutido a import?ncia do estudo de t?cnicas que possam auxiliar o desenvolvimento de sistemas de detec??o e diagn?stico de falhas e proposto um m?todo h?brido para a detec??o e diagn?stico de falhas em sistemas din?micos. Um breve hist?rico ? apresentado de forma a contextualizar as t?cnicas utilizadas no trabalho. A detec??o de falhas pelo sistema proposto ? baseada em observadores de estado juntamente com outras t?cnicas estat?sticas. A ideia principal ? utilizar o pr?prio observador, para al?m de servir como redund?ncia anal?tica, permitir a cria??o de um res?duo que ser? utilizado na detec??o da falha e tamb?m no seu diagn?stico. Um banco de assinaturas auxiliar? o sistema de identifica??o de falhas, que, baseado nas assinaturas oriundas das an?lises de tend?ncia do sinal do res?duo e sua derivada, ir? realizar a classifica??o das falhas baseada em uma ?rvore de decis?o. Este sistema FDD ser? submetido a alguns testes e valida??es em duas plantas: uma planta simulada de tanques acoplados e em uma planta did?tica com instrumenta??o industrial. Os resultados colhidos desses ensaios se mostraram satisfat?rios para um grupo de falhas testadas e ser?o discutidos no decorrer do trabalho

<p dir="ltr">Condition-based maintenance is a proactive maintenance strategy that prevents failures or diminished functionality in process systems through proper monitoring and management of process conditions. Despite being considered a mature maintenance management strategy in various industries, condition-based maintenance remains underutilized in pharmaceutical manufacturing. This situation needs to change, especially as the pharmaceutical industry continues to shift from batch to continuous manufacturing, where the implementation of CBM as a maintenance strategy assumes a greater importance.</p><p dir="ltr">This dissertation focused on addressing the challenges of implementing CBM in a continuous drug product manufacturing system. These challenges stem from the unique aspects of pharmaceutical drug product manufacturing, which includes the peculiar behavior of particulate materials and the evolutionary nature of pharmaceutical process development. The proposed solutions to address these challenges revolve around an innovative framework for the practical development of condition monitoring systems. Overall, this framework enables the incorporation of limited process knowledge in creating condition monitoring systems, which has the desired effect of empowering data-driven machine learning models.</p><p dir="ltr">A key feature of this framework is a formalized method to represent the process condition, which is usually vaguely defined in literature. This representation allows the proper mapping of preexisting condition monitoring systems, and the segmentation of the entire process condition model into smaller modules that have more manageable condition monitoring problems. Because this representation methodology is based on probabilistic graphical modelling, the smaller modules can then be holistically integrated via their probabilistic relationships, allowing the robust operation of the resulting condition monitoring system and the process it monitors.</p><p dir="ltr">Breaking down the process condition model into smaller segments is crucial for introducing novel fault detection capabilities, which enhances model prediction transparency and ensures prediction acceptance by a human operator. In this work, a methodology based on prediction probabilities was introduced for developing condition monitoring systems with novel fault detection capabilities. This approach relies on high-performing machine learning models capable of consistently classifying all the initially known conditions in the fault library with a high degree of certainty. Simplifying the condition monitoring problem through modularization facilitates this, as machine learning models tend to perform better on simpler systems. Performance indices were proposed to evaluate the novel fault detection capabilities of machine learning models, and a formal approach to managing novel faults was introduced.</p><p dir="ltr">Another benefit of modularization is the identification of condition monitoring blind spots. Applying it to the RC led to sensor development projects such as the virtual sensor for measuring granule flowability. This sensor concept was demonstrated successfully by using a data-driven model to predict granule flowability based on size and shape distribution measurements. With proper model selection and feature extraction guided by domain expertise, the resulting sensor achieved the best prediction performance reported in literature for granule flowability.</p><p dir="ltr">As a demonstration exercise in examining newly discovered faults, this work investigated a roll compaction phenomenon that is usually concealed from observation due to equipment design. This phenomenon results in the ribbon splitting along its thickness as it comes out of the rolls. In this work, important aspects of ribbon splitting were elucidated, particularly its predictability based on RC parameters and the composition of the powder blend used to form the ribbon. These findings have positive ramifications for the condition monitoring of the RC, as correspondence with industrial practitioners suggests that a split ribbon is desirable in some cases, despite being generally regarded as undesirable in the limited literature available on the subject.</p><p dir="ltr">Finally, this framework was primarily developed for the pharmaceutical dry granulation line, which consists of particle-based systems with a moderate level of complexity. However, it was also demonstrated to be feasible for the Tennessee Eastman Process (TEP), a more complex liquid-gas process system with a greater number of process faults, variables, and unit operations. Applying the framework resulted in machine learning models that yielded one of the best fault detection performances reported in literature for the TEP, while also introducing additional capabilities not yet normally reported in literature, such as fault diagnosis and novel fault detection.</p>

Tato disertační práce se zabývá využitím programovatelných hradlových polí (FPGA) v diagnostice měničů, využívajících spínaných IGBT tranzistorů. Je zaměřena na budiče těchto výkonových tranzistorů a jejich struktury. Přechodné jevy veličin, jako jsou IG, VGE, VCE během procesu přepínání (zapnutí, vypnutí), mohou poukazovat na degradaci IGBT. Pro měření a monitorování těchto veličin byla navržena nová architektura budiče IGBT. Rychlé měření a monitorování během přepínacího děje vyžaduje vysokou vzorkovací frekvenci. Proto jsou navrhovány paralelní vysokorychlostní AD převodníky (> 50 MSPS). Práce je zaměřena převážně na návrh zařízení s FPGA včetně hardware a software. Byla navržena nová deska plošných spojů s FPGA, která plní požadované funkce, jako je řízení IGBT pomocí vícenásobných paralelních koncových stupňů, monitorování a diagnostiku, a propojení s řídicí jednotkou měniče.