Submitted by Diogo Barreiros (diogo.barreiros@ufba.br) on 2017-02-17T16:41:33Z No. of bitstreams: 9 Capitulo I.pdf: 29639 bytes, checksum: 8007a2f7dfe2a8a68371ae454ff21265 (MD5) Capitulo II.pdf: 2398618 bytes, checksum: 67ec1b928bbd365a7a6ebc67b63a0cc3 (MD5) Capitulo III.pdf: 729546 bytes, checksum: 7778ce88975d7784a5c831e8af33cc12 (MD5) Capitulo IV.pdf: 39255 bytes, checksum: f005a001f7b707ab00d207d6718633b3 (MD5) Capitulo V.pdf: 424609 bytes, checksum: c070a2136bd33f21641c2d67d3b860d5 (MD5) Capitulo VI.pdf: 656297 bytes, checksum: 72bcce0a93526c29edd43b88091f3b94 (MD5) Capitulo VII.pdf: 1134199 bytes, checksum: 7fa6475029a4659167bab63ca25d6a37 (MD5) Capitulo VIII.pdf: 28984 bytes, checksum: 1c011016b6a3107609e9ebc952e018b4 (MD5) Elementos Pre-textuais.pdf: 78941 bytes, checksum: 38125074504363e58154371d872e9499 (MD5) / Approved for entry into archive by Vanessa Reis (vanessa.jamile@ufba.br) on 2017-02-21T11:56:14Z (GMT) No. of bitstreams: 9 Capitulo I.pdf: 29639 bytes, checksum: 8007a2f7dfe2a8a68371ae454ff21265 (MD5) Capitulo II.pdf: 2398618 bytes, checksum: 67ec1b928bbd365a7a6ebc67b63a0cc3 (MD5) Capitulo III.pdf: 729546 bytes, checksum: 7778ce88975d7784a5c831e8af33cc12 (MD5) Capitulo IV.pdf: 39255 bytes, checksum: f005a001f7b707ab00d207d6718633b3 (MD5) Capitulo V.pdf: 424609 bytes, checksum: c070a2136bd33f21641c2d67d3b860d5 (MD5) Capitulo VI.pdf: 656297 bytes, checksum: 72bcce0a93526c29edd43b88091f3b94 (MD5) Capitulo VII.pdf: 1134199 bytes, checksum: 7fa6475029a4659167bab63ca25d6a37 (MD5) Capitulo VIII.pdf: 28984 bytes, checksum: 1c011016b6a3107609e9ebc952e018b4 (MD5) Elementos Pre-textuais.pdf: 78941 bytes, checksum: 38125074504363e58154371d872e9499 (MD5) / Made available in DSpace on 2017-02-21T11:56:14Z (GMT). No. of bitstreams: 9 Capitulo I.pdf: 29639 bytes, checksum: 8007a2f7dfe2a8a68371ae454ff21265 (MD5) Capitulo II.pdf: 2398618 bytes, checksum: 67ec1b928bbd365a7a6ebc67b63a0cc3 (MD5) Capitulo III.pdf: 729546 bytes, checksum: 7778ce88975d7784a5c831e8af33cc12 (MD5) Capitulo IV.pdf: 39255 bytes, checksum: f005a001f7b707ab00d207d6718633b3 (MD5) Capitulo V.pdf: 424609 bytes, checksum: c070a2136bd33f21641c2d67d3b860d5 (MD5) Capitulo VI.pdf: 656297 bytes, checksum: 72bcce0a93526c29edd43b88091f3b94 (MD5) Capitulo VII.pdf: 1134199 bytes, checksum: 7fa6475029a4659167bab63ca25d6a37 (MD5) Capitulo VIII.pdf: 28984 bytes, checksum: 1c011016b6a3107609e9ebc952e018b4 (MD5) Elementos Pre-textuais.pdf: 78941 bytes, checksum: 38125074504363e58154371d872e9499 (MD5) / A necessidade de novas metodologias para análise e monitoramento de compressores alternativos motivou o desenvolvimento de um sistema digital multicanal com capacidade de medir diferentes grandezas simultaneamente. Como base para este trabalho escolheu-se medir pressão dinâmica, vibração e deslocamento do pistão. O desenvolvimento deste trabalho dentro de um curso de Mecatrônica produziu um sistema simples e eficiente cujos resultados experimentais, apresentados sob a forma de gráficos e dados de desempenho, confirmaram os defeitos introduzidos artificialmente. Isto permitiu validar o Sistema de Diagnóstico de Compressores Alternativos (SDCA) e demonstrou o quanto este recurso é poderoso para o diagnóstico de defeitos, tanto nos componentes da parte de compressão (fluid end) quanto nos de acionamento (power end). Estas informações, transformadas em recomendações de reparo, serão a base para uma atuação preditiva dos setores de Planejamento de Manutenção.

This thesis describes a proposed design and implementation of a predictive maintenance engine developed to fulfill the requirements of the STO Company (Societe de transport de l'Outaouais) for maintaining vehicles in the fleet. Predictive maintenance is proven to be an effective approach and has become an industry standard in many fields. However, in the transportation industry, it is still in the stages of development due to the complexity of moving systems and the high level dimensions of involved parameters. Because it is almost impossible to cover all use cases of the vehicle operational process using one particular approach to predictive maintenance, in our work we take a systematic approach to designing a predictive maintenance system in several steps. Each step is implemented at the corresponding development stage based on the available data accumulated during system funсtioning cycle. % by dividing the entire system into modules and implementing different approaches. This thesis delves into the process of designing the general infrastructural model of the fleet management system (FMS), while focusing on the edge gateway module located on the vehicle and its function of detecting maintenance events based on current vehicle status. Several approaches may be used to detect maintenance events, such as a machine learning approach or an expert system-based approach. While the final version of fleet management system will use a hybrid approach, in this thesis paper we chose to focus on the second option based on expert knowledge, while machine learning has been left for future implementation since it requires extensive training data to be gathered prior to conducting experiments and actualizing operations. Inspired by the IDEA methodology which promotes mapping business rules as software classes and using the object-relational model for mapping objects to database entities, we take active database features as a base for developing a rule engine implementation. However, in contrast to the IDEA methodology which seeks to describe the specific system and its sub-modules, then build active rules based on the interaction between sub-systems, we are not aware of the functional structure of the vehicle due to its complexity. Instead, we develop a framework for creating specific active rules based on abstract classifications structured as ECA rules (event-condition-action), but with some expansions made due to the specifics of vehicle maintenance. The thesis describes an attempt to implement such a framework, and particularly the rule engine module, using active database features making it possible to encapsulate the active behaviour inside the database and decouple event detection from other functionalities. We provide the system with a set of example rules and then conduct a series of experiments analyzing the system for performance and correctness of events detection.

This thesis deals with the description and innovation in an industrial company, which is focused on the diagnostics of production machines using mainly big data collection. The theoretical part of the thesis describes industry 4.0, the demands of big data on storage and also the basics of change management. The analytical part includes a description of the company, an analysis of the current state and a proposal for change. The proposed changes are the transition from a relational database to a non relational one and a new web application for data visualization. The last part of the thesis describes the implementation of these changes and their evaluation.

L'augmentation des données disponibles dans presque tous les domaines soulève la nécessité d'utiliser des algorithmes pour l'analyse automatisée des données. Cette nécessité est mise en évidence dans la maintenance prédictive, où l'objectif est de prédire les pannes des systèmes en observant continuellement leur état, afin de planifier les actions de maintenance à l'avance. Ces observations sont générées par des systèmes de surveillance habituellement sous la forme de séries temporelles et de journaux d'événements et couvrent la durée de vie des composants correspondants. Le principal défi de la maintenance prédictive est l'analyse de l'historique d'observation afin de développer des modèles prédictifs.Dans ce sens, l'apprentissage automatique est devenu omniprésent puisqu'il fournit les moyens d'extraire les connaissances d'une grande variété de sources de données avec une intervention humaine minimale. L'objectif de cette thèse est d'étudier et de résoudre les problèmes dans l'aviation liés à la prévision des pannes de composants à bord. La quantité de données liées à l'exploitation des avions est énorme et, par conséquent, l'évolutivité est une condition essentielle dans chaque approche proposée.Cette thèse est divisée en trois parties qui correspondent aux différentes sources de données que nous avons rencontrées au cours de notre travail. Dans la première partie, nous avons ciblé le problème de la prédiction des pannes des systèmes, compte tenu de l'historique des Post Flight Reports. Nous avons proposé une approche statistique basée sur la régression précédée d'une formulation méticuleuse et d'un prétraitement / transformation de données. Notre méthode estime le risque d'échec avec une solution évolutive, déployée dans un environnement de cluster en apprentissage et en déploiement. À notre connaissance, il n'y a pas de méthode disponible pour résoudre ce problème jusqu'au moment où cette thèse a été écrite.La deuxième partie consiste à analyser les données du livre de bord, qui consistent en un texte décrivant les problèmes d'avions et les actions de maintenance correspondantes. Le livre de bord contient des informations qui ne sont pas présentes dans les Post Flight Reports bien qu'elles soient essentielles dans plusieurs applications, comme la prédiction de l'échec. Cependant, le journal de bord contient du texte écrit par des humains, il contient beaucoup de bruit qui doit être supprimé afin d'extraire les informations utiles. Nous avons abordé ce problème en proposant une approche basée sur des représentations vectorielles de mots. Notre approche exploite des similitudes sémantiques, apprises par des neural networks qui ont généré les représentations vectorielles, afin d'identifier et de corriger les fautes d'orthographe et les abréviations. Enfin, des mots-clés importants sont extraits à l'aide du Part of Speech Tagging.Dans la troisième partie, nous avons abordé le problème de l'évaluation de l'état des composants à bord en utilisant les mesures des capteurs. Dans les cas considérés, l'état du composant est évalué par l'ampleur de la fluctuation du capteur et une tendance à l'augmentation monotone. Dans notre approche, nous avons formulé un problème de décomposition des séries temporelles afin de séparer les fluctuations de la tendance en résolvant un problème convexe. Pour quantifier l'état du composant, nous calculons à l'aide de Gaussian Mixture Models une fonction de risque qui mesure l'écart du capteur par rapport à son comportement normal. / The increase of available data in almost every domain raises the necessity of employing algorithms for automated data analysis. This necessity is highlighted in predictive maintenance, where the ultimate objective is to predict failures of hardware components by continuously observing their status, in order to plan maintenance actions well in advance. These observations are generated by monitoring systems usually in the form of time series and event logs and cover the lifespan of the corresponding components. Analyzing this history of observation in order to develop predictive models is the main challenge of data driven predictive maintenance.Towards this direction, Machine Learning has become ubiquitous since it provides the means of extracting knowledge from a variety of data sources with the minimum human intervention. The goal of this dissertation is to study and address challenging problems in aviation related to predicting failures of components on-board. The amount of data related to the operation of aircraft is enormous and therefore, scalability is a key requirement in every proposed approach.This dissertation is divided in three main parts that correspond to the different data sources that we encountered during our work. In the first part, we targeted the problem of predicting system failures, given the history of Post Flight Reports. We proposed a regression-based approach preceded by a meticulous formulation and data pre-processing/transformation. Our method approximates the risk of failure with a scalable solution, deployed in a cluster environment both in training and testing. To our knowledge, there is no available method for tackling this problem until the time this thesis was written.The second part consists analyzing logbook data, which consist of text describing aircraft issues and the corresponding maintenance actions and it is written by maintenance engineers. The logbook contains information that is not reflected in the post-flight reports and it is very essential in several applications, including failure prediction. However, since the logbook contains text written by humans, it contains a lot of noise that needs to be removed in order to extract useful information. We tackled this problem by proposing an approach based on vector representations of words (or word embeddings). Our approach exploits semantic similarities of words, learned by neural networks that generated the vector representations, in order to identify and correct spelling mistakes and abbreviations. Finally, important keywords are extracted using Part of Speech Tagging.In the third part, we tackled the problem of assessing the health of components on-board using sensor measurements. In the cases under consideration, the condition of the component is assessed by the magnitude of the sensor's fluctuation and a monotonically increasing trend. In our approach, we formulated a time series decomposition problem in order to separate the fluctuation from the trend by solving a convex program. To quantify the condition of the component, we compute a risk function which measures the sensor's deviation from it's normal behavior, which is learned using Gaussian Mixture Models.

A cyclotron is used for diagnosing and treating cancer. Pipes in the cyclotron have to be replaced as they get worn out when isotopes travel through them. This thesis aims to use machine learning models to predict when these parts have to be changed. Based on previous studies for predictive maintenance three dif- ferent machine learning models are used. The chosen models are random forest, gradient boosting and support vector machine. The results show that a gradient boosting regressor that predicts the number of remaining runs before the pipes have to be changed in the cyclotron is preferred. However, some data augmenta- tion had to be done to obtain these results, and future studies could explore the possibility of using a bigger data set or a multiple classifier approach.

Serienfertiger im Bereich der Innenhochdruck-Umformung stehen unter starkem Wettbewerbsdruck alternativer klassischer Fertigungen und deren Kostenkriterien. Wechselnde Produktionsanforderungen im globalisierten Marktumfeld erfordern flexibles Handeln bei höchster Qualität und niedrigen Kosten. Durch Reduzierung der Lager- und Umlaufbestände können Kosteneinsparungen erzielt werden. Störungsbedingte Ausfälle an IHU-Anlagen gilt es dabei auf ein Minimum zu reduzieren, um die vereinbarten Liefertermine fristgerecht zu erfüllen und Konventionalstrafen zu umgehen. Die erforderliche Produktivität und das angestrebte Qualitätsniveau lässt sich nur durch angepasste Instandhaltungsstrategien aufrechterhalten, weshalb ein Konzept für die vorausschauende Instandhaltung mit integriertem Qualitätsmanagement speziell für den Bereich der IHU erarbeitet wurde. Dynamische Prozess- und Instandhaltungsanpassungen sind zentraler Bestandteil der Entwicklungsarbeit.

Series manufacturers in the field of hydroforming face intense competition from alternative conventional manufacturing methods and their cost criteria. Changing production requirements in the globalized market environment require flexible action with highest quality and low costs. Cost savings can be achieved through reductions in warehouse and circulating stocks. Malfunction-related downtimes in hydroforming systems must be reduced to a minimum in order to meet the agreed delivery dates on time and avoid conventional penalties. The required productivity and the desired quality level can only be maintained through adapted maintenance strategies, leading to the development of a concept for predictive maintenance integrated with quality management specifically for the IHU domain. Dynamic process and maintenance adaptations are a central component to this developmental effort.

Metall- und Maschinenbauunternehmen müssen im Durchschnitt pro Jahr ca. zwei Prozent ihres Umsatzes für Strom und Erdgas ausgeben und die Unternehmer gehen von weiteren Preissteigerungen aus. Damit rückt das Thema Energieeinsparung stärker denn je in den Fokus und wird zu einem strategischen Faktor. Um Kosten zu sparen und Wettbewerbsvorteile zu sichern, ist es notwendig, zielgenaue Energieeinsparmaßnahmen einzuleiten. Die ersten Maßnahmen, welche die meisten Maschinenbauunternehmen umsetzen, sind die Erneuerung der Beleuchtungs-, Heizungs- und Lüftungsanlage, die Verbesserung der Drucklufterzeugung sowie die thematische Sensibilisierung der Mitarbeiter. Aber auch in Maschinen mit ihren dazugehörigen elektrischen Antrieben, Lüftern und Aggregaten verbirgt sich eine große Menge an Optimierungspotenzial. Allerdings ist es hier notwendig nicht die Verbraucher im Einzelnen, sondern die Maschine und deren Prozesse im Ganzen zu betrachten. Meist fehlen hierfür aber geeignete Schnittstellen, um die Messwerte von Sensoren (bspw. Temperatur-, Drucksensoren, etc.) und Antrieben auslesen zu können, was dazu führt, dass diese Potenziale nicht ausgeschöpft werden.

The average metal and mechanical engineering company must spend around two per cent of its annual turnover on electricity and natural gas, and companies are expecting further price increases. As a result, the issue of energy saving is becoming more of a strategic factor than ever before. In order to save costs and ensure competitive advantages, it is necessary to introduce precise energy-saving measures. The first steps taken by most mechanical engineering companies are to replace lighting, heating, and ventilation systems, improve compressed air generation and raise employee awareness. However, there is also a great potential for optimization in machines with their individual electrical drives, fans, and units. In this case, though, it is necessary to look at the machine and its processes as a whole rather than the individual electrical energy consumers. In most cases, however, there is a lack of suitable interfaces for analyzing the measured values from sensors (e.g. temperature, pressure sensors, etc.) and drives, which concludes that this potential is not fully exploited.

ITC/USA 2008 Conference Proceedings / The Forty-Fourth Annual International Telemetering Conference and Technical Exhibition / October 27-30, 2008 / Town and Country Resort & Convention Center, San Diego, California / Predictive maintenance is the combination of inspection and data analysis to perform maintenance when the need is indicated by unit performance. Significant cost savings are possible while preserving a high level of system performance and readiness. Identifying predictors of maintenance conditions requires expert knowledge and the ability to process large data sets. This paper describes a novel use of constraint-based data-mining to model exceedence conditions. The approach extends the extract, transformation, and load process with domain aggregate approximation to encode expert knowledge. A data-mining workbench enables an expert to pose hypotheses that constrain a multivariate data-mining process.