The diagnosis system of a Scania vehicle is an indispensable tool for workshop personnel and engineers in their work. Today Scania has a system for fetching diagnostic information from field test vehicles remotely and store them in a database, so called remote diagnosis. This saves the engineers much time by not having to visit every vehicle. The system uses a Windows based on-board PC in the vehicle called an Interactor. The Interactor has a telematic unit for communication with Scanias Fleet Management System and the CAN-bus in the vehicle. In the next generation of the Interactor, its telematic unit is to be replaced by a Linux based telematic unit called the Communicator 200 (C200). The purpose of this master project is to create a new architecture for a remote diagnosis system that uses the new telematic unit Communicator 200. <br />The thesis gives an analysis of the current remote diagnosis system used at Scania and proposes an architecture for a new generation remote diagnosis system using the C200. Also a system for demonstrating how to perform remote diagnosis over the C200 has been built. The thesis describes the operation and how the demonstration system was implemented.

To troubleshoot equipment installed in geographically distant locations, equipment manufacturers and system integrators are increasingly resorting to remote diagnosis in order to reduce the down time of the equipment, thereby achieving savings in cost and time on both the customer and manufacturer side. Remote diagnosis involves the use of communication technologies to perform fault diagnosis of a system located at a site distant to a troubleshooter. In order to achieve remote diagnosis, several frameworks have been proposed incorporating advancements such as automated fault diagnosis, collaborative diagnosis and mobile communication techniques. Standards exist for the capabilities representative of different levels of remote equipment diagnosis. Several studies have been performed to analyze the ability of human machine interface to assist troubleshooters in local fault diagnosis. However, the ability of a remote diagnosis system architecture to assist the troubleshooter in performing diagnosis and the effects of the failure types and other factors in a remote diagnosis environment on remote troubleshooting performance are not frequently addressed. In this thesis, an attempt is made to understand the factors that affect remote troubleshooting performance: remote diagnosis architecture, nature of failure, skill level of the local operator and level of expertise of the remote troubleshooter. For this purpose, three hierarchical levels of remote diagnosis architectures to diagnose failures in a PLC based automated assembly system were built based on existing standards. Common failures in automated assembly systems were identified and duplicated. Experiments were performed in which expert and novice troubleshooters used these remote diagnosis architectures to diagnose different types of failures while working with novice and engineer operators. The results suggest that in the diagnosis of failures related to measured or monitored system variables by remote expert troubleshooters, remote troubleshooting performance improved with the increase in the levels of the remote diagnosis architectures. In contrast, in the diagnosis of these failures by novice troubleshooters, no significant difference was observed among the three architectures in terms of remote troubleshooting performance and the novice troubleshooters experienced problems with managing the increased information available. Failures unrelated to monitored system parameters resulted in significantly reduced remote troubleshooting performance with all the three architectures in comparison to the failures related to monitored system parameters for both expert and novice troubleshooters. The experts exhibited better information gathering capabilities by spending more time per information source and making fewer transitions between information sources while diagnosing failures. The increase in capabilities of the architectures resulted in reduced operator interaction to a to a greater extent with experts. The difference in terms of overall remote troubleshooting performance between engineer and novice operators was not found to be significant.

<p>The diagnosis system of a Scania vehicle is an indispensable tool for workshop personnel and engineers in their work. Today Scania has a system for fetching diagnostic information from field test vehicles remotely and store them in a database, so called remote diagnosis. This saves the engineers much time by not having to visit every vehicle. The system uses a Windows based on-board PC in the vehicle called an Interactor. The Interactor has a telematic unit for communication with Scanias Fleet Management System and the CAN-bus in the vehicle. In the next generation of the Interactor, its telematic unit is to be replaced by a Linux based telematic unit called the Communicator 200 (C200). The purpose of this master project is to create a new architecture for a remote diagnosis system that uses the new telematic unit Communicator 200.</p><p><br />The thesis gives an analysis of the current remote diagnosis system used at Scania and proposes an architecture for a new generation remote diagnosis system using the C200. Also a system for demonstrating how to perform remote diagnosis over the C200 has been built. The thesis describes the operation and how the demonstration system was implemented.</p>

O gênero Eimeria compreende um grupo de protozoários da classe Coccidia que infecta uma grande variedade de hospedeiros. Um total de sete espécies distintas Eimeria podem infectar a galinha doméstica causando enterites com graves prejuízos econômicos. A identificação das espécies pode ser feita através da análise microscópica das diferentes características morfológicas dos oocistos, um dos estágios de desenvolvimento do parasita. Alternativamente, ensaios moleculares baseados na amplificação de alvos específicos de DNA também podem ser utilizados. Em ambos os casos, requer-se um laboratório especializado e, principalmente, pessoal altamente treinado. Neste trabalho é relatada uma abordagem computacional para a extração automática de características para a representação da forma das distintas espécies de Eimeria. Foram utilizadas imagens digitais do protozoário nas quais aplicou-se técnicas de processamento de imagens e visão computacional para sua representação morfológica, formando três grupos de características: medidas geométricas, caracterização da curvatura, e quantificação da estrutura interna. A morfologia dos protozoários foi representada por um vetor de características constituído por 14 dimensões, o qual constituiu o padrão de entrada para o processo de classificação. Para o reconhecimento dos padrões, foram usados dois classificadores Bayesianos, utilizando-se como funções de verossimilhança a Gaussiana e a de Dirichlet, respectivamente. O primeiro classificador apresentou as melhores taxas de acerto, enquanto o segundo demonstrou melhor desempenho segundo a análise por curvas ROC. Como prova de princípio de que o sistema poderia ser utilizado por usuários leigos para o diagnóstico à distância de parasitas, foi implementado o COCCIMORPH, um sistema de diagnóstico de Eimeria em tempo real. O sistema permite o envio de imagens via web, assim como o seu pré-processamento e classificação remotos, obtendo-se o resultado do diagnóstico em tempo real. Essa abordagem totalmente integrada e implementada é inédita para o diagnóstico de parasitas. Entre suas vantagens principais está o fato de que o diagnóstico pode ser obtido sem a necessidade do transporte físico de amostras biológicas para um laboratório de referência, evitando assim riscos de contaminação do ambiente. Para o treinamento do sistema, foram obtidas centenas de micrografias de cada uma das sete espécies de Eimeria que infectam a galinha doméstica. Essas imagens também foram usadas para a construção de um banco de acesso público de imagens (The Eimeria Image Database). Além disso, a metodologia de diagnóstico foi também aplicada e testada com onze espécies Eimeria de coelho doméstico. Com isso, foram gerados dados inéditos de morfometria, micrografias adicionais para o banco de imagens, e um sistema de classificação para esse conjunto adicional de parasitas. Finalmente, foram determinadas as distâncias entre as diferentes espécies de Eimeria, calculadas a partir dos dados morfométricos. As árvores de distância revelaram uma topologia muito similar com árvores obtidas a partir da inferência filogenética usando-se marcadores moleculares como o gene 18S de rRNA ou genomas mitocondriais. / The Eimeria genus comprises a group of protozoan parasites that infect a wide range of hosts. A total of seven different Eimeria species infect the domestic fowl, causing enteritis with severe economical losses. Species identification can be performed through microscopic analysis of the distinct morphological characteristics of the oocysts, a developmental stage of the parasite. Alternatively, molecular assays based on the amplification of specific DNA targets can also be used. In both cases, a well equipped laboratory and, especially, highly qualified personnel are required. In this work, we report a computational approach for the automatic feature extraction for shape representation of the different Eimeria species. Digital images of the parasites were used in order to apply image processing and computational vision techniques for shape characterization. Three groups of morphological features were constituted: geometric measures, curvature characterization, and internal structure quantification. The protozoan morphology was represented by a 14-dimension feature vector, which was used as the input pattern for the classification process. Two Bayesian classifiers were used for pattern recognition, using as a likelihood function the normal and the Dirichlet, respectively. The former classifier presented the best correct classification rates, whereas the latter showed a better performance in ROC curve analyses. As a proof of principle that this system could be utilized by end-users for a long-distance parasite diagnosis, we implemented COCCIMORPH, an integrated system for the real-time diagnosis of Eimeria spp. The system presents an interface for image uploading. Image preprocessing and diagnosis are performed remotely and the results displayed in real-time. This fully integrated and implemented system constitutes a novel approach for parasite diagnosis. Among the several advantages of the system, it is noteworthy that no biological sample transportation is required between the farm and the reference laboratory, thus avoiding potential environment contamination risks. To train the system, we used hundreds of micrographs of each one of the seven Eimeria species of domestic fowl. These images were used to compose a public image repository (The Eimeria Image Database). In addition, our diagnosis methodology was extended to the eleven Eimeria species that infect the domestic rabbit. With this integrated approach, a totally novel set of images and morphometric data of rabbit Eimeria were incorporated to the image database and, also to the remote diagnosis system. Finally, distance trees of the distinct Eimeria species of domestic fowl were computed from the morphometric data. The trees revealed a very similar topology with trees obtained with molecular phylogenetic markers such as the 18S rRNA gene and mitochondrial genomes.

O gênero Eimeria compreende um grupo de protozoários da classe Coccidia que infecta uma grande variedade de hospedeiros. Um total de sete espécies distintas Eimeria podem infectar a galinha doméstica causando enterites com graves prejuízos econômicos. A identificação das espécies pode ser feita através da análise microscópica das diferentes características morfológicas dos oocistos, um dos estágios de desenvolvimento do parasita. Alternativamente, ensaios moleculares baseados na amplificação de alvos específicos de DNA também podem ser utilizados. Em ambos os casos, requer-se um laboratório especializado e, principalmente, pessoal altamente treinado. Neste trabalho é relatada uma abordagem computacional para a extração automática de características para a representação da forma das distintas espécies de Eimeria. Foram utilizadas imagens digitais do protozoário nas quais aplicou-se técnicas de processamento de imagens e visão computacional para sua representação morfológica, formando três grupos de características: medidas geométricas, caracterização da curvatura, e quantificação da estrutura interna. A morfologia dos protozoários foi representada por um vetor de características constituído por 14 dimensões, o qual constituiu o padrão de entrada para o processo de classificação. Para o reconhecimento dos padrões, foram usados dois classificadores Bayesianos, utilizando-se como funções de verossimilhança a Gaussiana e a de Dirichlet, respectivamente. O primeiro classificador apresentou as melhores taxas de acerto, enquanto o segundo demonstrou melhor desempenho segundo a análise por curvas ROC. Como prova de princípio de que o sistema poderia ser utilizado por usuários leigos para o diagnóstico à distância de parasitas, foi implementado o COCCIMORPH, um sistema de diagnóstico de Eimeria em tempo real. O sistema permite o envio de imagens via web, assim como o seu pré-processamento e classificação remotos, obtendo-se o resultado do diagnóstico em tempo real. Essa abordagem totalmente integrada e implementada é inédita para o diagnóstico de parasitas. Entre suas vantagens principais está o fato de que o diagnóstico pode ser obtido sem a necessidade do transporte físico de amostras biológicas para um laboratório de referência, evitando assim riscos de contaminação do ambiente. Para o treinamento do sistema, foram obtidas centenas de micrografias de cada uma das sete espécies de Eimeria que infectam a galinha doméstica. Essas imagens também foram usadas para a construção de um banco de acesso público de imagens (The Eimeria Image Database). Além disso, a metodologia de diagnóstico foi também aplicada e testada com onze espécies Eimeria de coelho doméstico. Com isso, foram gerados dados inéditos de morfometria, micrografias adicionais para o banco de imagens, e um sistema de classificação para esse conjunto adicional de parasitas. Finalmente, foram determinadas as distâncias entre as diferentes espécies de Eimeria, calculadas a partir dos dados morfométricos. As árvores de distância revelaram uma topologia muito similar com árvores obtidas a partir da inferência filogenética usando-se marcadores moleculares como o gene 18S de rRNA ou genomas mitocondriais. / The Eimeria genus comprises a group of protozoan parasites that infect a wide range of hosts. A total of seven different Eimeria species infect the domestic fowl, causing enteritis with severe economical losses. Species identification can be performed through microscopic analysis of the distinct morphological characteristics of the oocysts, a developmental stage of the parasite. Alternatively, molecular assays based on the amplification of specific DNA targets can also be used. In both cases, a well equipped laboratory and, especially, highly qualified personnel are required. In this work, we report a computational approach for the automatic feature extraction for shape representation of the different Eimeria species. Digital images of the parasites were used in order to apply image processing and computational vision techniques for shape characterization. Three groups of morphological features were constituted: geometric measures, curvature characterization, and internal structure quantification. The protozoan morphology was represented by a 14-dimension feature vector, which was used as the input pattern for the classification process. Two Bayesian classifiers were used for pattern recognition, using as a likelihood function the normal and the Dirichlet, respectively. The former classifier presented the best correct classification rates, whereas the latter showed a better performance in ROC curve analyses. As a proof of principle that this system could be utilized by end-users for a long-distance parasite diagnosis, we implemented COCCIMORPH, an integrated system for the real-time diagnosis of Eimeria spp. The system presents an interface for image uploading. Image preprocessing and diagnosis are performed remotely and the results displayed in real-time. This fully integrated and implemented system constitutes a novel approach for parasite diagnosis. Among the several advantages of the system, it is noteworthy that no biological sample transportation is required between the farm and the reference laboratory, thus avoiding potential environment contamination risks. To train the system, we used hundreds of micrographs of each one of the seven Eimeria species of domestic fowl. These images were used to compose a public image repository (The Eimeria Image Database). In addition, our diagnosis methodology was extended to the eleven Eimeria species that infect the domestic rabbit. With this integrated approach, a totally novel set of images and morphometric data of rabbit Eimeria were incorporated to the image database and, also to the remote diagnosis system. Finally, distance trees of the distinct Eimeria species of domestic fowl were computed from the morphometric data. The trees revealed a very similar topology with trees obtained with molecular phylogenetic markers such as the 18S rRNA gene and mitochondrial genomes.