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Artificial neural networks (ANNs) have been used in many fault detection and identification (FDI) applications due to their pattern recognition abilities. In this study, two ANNs, a supervised network based on Backpropagation (BP) learning and an unsupervised network based on Adaptive Resonance Theory (ART-2A), were tested for FDI on an automated assembly machine and compared to a conventional rule-based method. Three greyscale sensors and two redundant limit switches were used as cost-effective sensors to monitor the machine's operating condition. To test each method, sensor data were collected while the machine operated under normal conditions, as well as 10 fault conditions. Features were selected from the raw sensor data to create data sets for training and testing. The performance of the methods was evaluated with respect to their ability to detect and identify known, unknown and multiple faults. Their modelling and computational requirements were also considered as performance measures. Results showed that all three methods were able to achieve perfect classification with the test data sets; however, the BP method could not classify unknown or multiple faults. In all cases, the performance depended on careful tuning of each method’s parameters. The BP method required an ideal number of neurons in the hidden layer and good initialization. The ART-2A method required tuning of its classification parameter. The rule-based method required tuning of its thresholds. Although it was found that the rule-based system required more effort to set up, it was judged to be more useful when unknown or multiple faults were present. The ART-2A network created new outputs for these conditions, but it could not give any more information as to what the new fault was. By contrast, the rule-based method was able to generate symptoms that clearly identified the unknown and multiple fault conditions. Thus, the rule-based method was judged to be the best overall method for this type of application. It is recommended that future work examine the application of computer vision-based techniques to FDI with the assembly machine. The results from this study, using cost-effective sensors, could then be used as a performance benchmark for image-based sensors. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2014-05-16 17:21:13.676

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Thesis (MEng) -- Stellenbosch University, 2014. / ENGLISH ABSTRACT: Modern mineral processing plants are characterised by a large number of measured variables, interacting through numerous processing units, control loops and often recycle streams. Consequentially, faults in these plants propagate throughout the system, causing significant degradation in performance. Fault diagnosis therefore forms an essential part of performance monitoring in such processes. The use of feature extraction methods for fault diagnosis has been proven in literature to be useful in application to chemical or minerals processes. However, the ability of these methods to identify the causes of the faults is limited to identifying variables that display symptoms of the fault. Since faults propagate throughout the system, these results can be misleading and further fault identification has to be applied. Faults propagate through the system along material, energy or information flow paths, therefore process topology information can be used to aid fault identification. Topology information can be used to separate the process into multiple blocks to be analysed separately for fault diagnosis; the change in topology caused by fault conditions can be exploited to identify symptom variables; a topology map of the process can be used to trace faults back from their symptoms to possible root causes. The aim of this project, therefore, was to develop a process monitoring strategy that exploits process topology for fault detection and identification. Three methods for extracting topology from historical process data were compared: linear cross-correlation (LC), partial cross-correlation (PC) and transfer entropy (TE). The connectivity graphs obtained from these methods were used to divide process into multiple blocks. Two feature extraction methods were then applied for fault detection: principal components analysis (PCA), a linear method, was compared with kernel PCA (KPCA), a nonlinear method. In addition, three types of monitoring chart methods were compared: Shewhart charts; exponentially weighted moving average (EWMA) charts; and cumulative sum (CUSUM) monitoring charts. Two methods for identifying symptom variables for fault identification were then compared: using contributions of individual variables to the PCA SPE; and considering the change in connectivity. The topology graphs were then used to trace faults to their root causes. It was found that topology information was useful for fault identification in most of the fault scenarios considered. However, the performance was inconsistent, being dependent on the accuracy of the topology extraction. It was also concluded that blocking using topology information substantially improved fault detection and fault identification performance. A recommended fault diagnosis strategy was presented based on the results obtained from application of all the fault diagnosis methods considered. / AFRIKAANSE OPSOMMING: Moderne mineraalprosesseringsaanlegte word gekarakteriseer deur ʼn groot aantal gemete veranderlikes, wat in wisselwerking tree met mekaar deur verskeie proseseenhede, beheerlusse en hersirkulasiestrome. As gevolg hiervan kan foute in aanlegte deur die hele sisteem propageer, wat prosesprestasie kan laat afneem. Foutdiagnose vorm dus ʼn noodsaaklike deel van prestasiemonitering. Volgens literatuur is die gebruik van kenmerkekstraksie metodes vir foutdiagnose nuttig in chemiese en mineraalprosesseringsaanlegte. Die vermoë van hierdie metodes om die fout te kan identifiseer is egter beperk tot die identifikasie van veranderlikes wat simptome van die fout vertoon. Aangesien foute deur die sisteem propageer kan resultate misleidend wees, en moet verdere foutidentifikasie metodes dus toegepas word. Foute propageer deur die proses deur materiaal-, energie- of inligtingvloeipaaie, daarom kan prosestopologie inligting gebruik word om foutidentifikasie te steun. Topologie inligting kan gebruik word om die proses in veelvoudige blokke te skei om die blokke apart te ontleed. Die verandering in topologie veroorsaak deur fouttoestande kan dan analiseer word om simptoomveranderlikes te identifiseer. ʼn Topologiekaart van die proses kan ontleed word om moontlike hoofoorsake van foute op te spoor. Die doel van hierdie projek was dus om ʼn prosesmoniteringstrategie te ontwikkel wat prosestopologie benut vir fout-opspooring en foutidentifikasie. Drie metodes vir topologie-ekstraksie van historiese prosesdata is met mekaar vergelyk: liniêre kruiskorrelasie, parsiële kruiskorrelasie en oordrag-entropie. Konnektiwiteitsgrafieke verkry deur hierdie ekstraksie-metodes is gebruik om die proses in veelvoudige blokke te skei. Twee kenmerkekstraksiemetodes is hierna toegepas om foutdeteksie te bewerkstellig: hoofkomponentanalise (HKA), ʼn liniêre metode; en kernhoofkomponentanalise (KHKA), ʼn nie-lineêre metode. Boonop was drie tipes moniteringskaart metodes vergelyk: Shewhart kaarte, eksponensieel-geweegde bewegende gemiddelde kaarte en kumulatiewe som kaarte. Twee metodes om simptoom veranderlikes te identifiseer vir foutidentifikasie was daarna vergelyk: gebruik van individuele veranderlikes; en inagneming van die verandering in konnektiwiteit. Die konnektiwiteitgrafieke was daarna gebruik om hoofoorsake van foute op te spoor. Dit is gevind dat topologie informasie nuttig was vir foutidentifikasie vir meeste van die fouttoestande ondersoek. Nogtans was die prestasie onsamehangend, aangesien dit afhanklik is van die akkuraatheid waarmee topologie ekstraksie uitgevoer is. Daar was ook afgelei dat die gebruik van topologie blokke beduidend die fout-opspooring en foutidentifikasie prestasie verbeter het. ʼn Aanbevole foutdiagnose strategie is voorgestel.

Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-01-24Bitstream added on 2014-06-13T20:48:01Z : No. of bitstreams: 1 pinheiro_jrg_me_bauru.pdf: 4191682 bytes, checksum: d4e7ba9da361bba449aa5305e441f61c (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O sistema de distribuiçã de energia elétrica no Brasil é constituído, na grande maioria, de alimentadores aéreos na classe 15 kV sujeitos a muitos tipos de defeitos. Embora existam muitos trabalhos propondo técnicas para a identificação e a localização das faltas, a maioria deles foi desenvolvida objetivando as redes de transmissão pouco ramificadas. Em se tratando de redes muito ramificadas e extensas, características princiapais de redes de distribuição elétrica, esses métodos não apresentam alta confiabilidade e segurança em termos de detecção, dificultando a localização das faltas. Este trabalho tem como objetivo descrever o desenvolvimento de um sistema de medição de baixo custo voltado para a monitoração de alimentadores aéreosm de distribuição de energia elétrica, que possibilite a identificação e a localização de faltas bem como a avaliação da qualidade da energia elétrica fornecida. Para tal, uma rede de sensores sem fio no padrão IEEE 802.15.4 é utilizada para adquirir os dados de tensão e corrente de cada frase e, a partir do processamento dessas informações, possibilitar a identificação de um ramal sob falta e a determinação do seu tipo. Com a instação de mediadores em pontos onde a rede de distribuição se ramifica, o ramal sob falta pode ser localizado, reduzindo assim o tempo de desligamento e os custos de manutenção / The system of eletrecity distribution in Brazil is made mostly of air handlers in the class 15 kV, subject to many kinds of defects. Although there are many papers proposing techniques for the identification and location of faults, most of them were developed aiming at the broadcast networks little branched. In terms of networks very extensive and branched, like in distribution networks, these methods have low reliability and safety in terms of detection, makind the location of faults difficult. This paper aims to describe the development of a measurment system focused on low-cost monitoring of overhead distribution feeders of electricity, allowing for the identification and location of faults and the quality of power supplied. For this purpose, a network of wireless sensors on the IEEE 208.15.4 is used for data acquisition of each phase voltage and current and from the processing of such information is possible to determine a faulted extension and identification of its type. With the installation of meters at points where the distribution networks branches, the faulted extension can be located, thereby reducing the shutdown time and maintenance costs

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Orientador : Vicente Lopes Júnior / Banca: João Carlos Mendes Carvalho / Banca: Carlos Roberto Minussi / Resumo: A proposta deste trabalho é a obtenção de uma metodologia robusta para identificação de falhas estruturais combinando as vantagens de duas metodologias, que não são baseadas em modelos matemáticos, ou seja: impedância elétrica obtida com atuador e sensor piezocerâmico(materiais inteligentes) e redes neurais artificiais. O termo materiais inteligentes (smart materials) conhecido também por material ativo é dado a uma classe de material que exibe propriedades não encontradas em materiais convencionais. Alguns destes materiais são: compostos de materiais piezelétricos, eletrorresistivo e magnetorresistivo, fluidos e sólidos electro-reológicos, e outros. Uma das principais características do PZT (Titanato Zirconato de Chumbo), que permite utilizá-lo como sensor e atuador, é o efeito piezelétrico, ou seja, a aplicação de um campo elétrico resulta em deformação do material (efeito inverso), enquanto, a aplicação de tensão mecânica resulta no surgimento de um campo elétrico (efeito direto). Estas características associadas ao conceito de impedância elétrica e ao conceito de falha métrica permitem a localização e o monitoramento da falha. Esta técnica utiliza altas freqüências e excita os modos locais, proporcionando, assim, o monitoramento de qualquer mudança da impedância mecânica estrutural na região de influência do PZT. Redes neurais artificiais (RNA) fazem parte de um amplo conceito chamado inteligência artificial. Redes neurais têm sua base associada ao funcionamento do cérebro humano, que após treinamento possuem a capacidade de "aprender". Esta ciência é objeto de estudo em diversos centros de pesquisa e, embora já tenha grande aplicabilidade, o sucesso de sua utilização depende do caso em que está sendo aplicada e de certa sutileza do projetista, uma vez que o processo ainda é empírico e teorias ainda... (Resumo completo, clicar acesso eletrônico abaixo). / Abstract: The proposal of this work is the obtaining of a robust methodology for identification of structural faults combining the advantages of two methodologies, which are not based on mathematical models. The methodology applies electric impedance technique, obtained with actuator and sensor piezoceramic (smart materials), and artificial neural networks. The term "smart materials" is given for a material class that not exhibits properties found in conventional materials. Some of these materials are: composed of piezoelectric material, electrostrictive and magnetostrictive, electrorheological fluids and solids shape memory alloys, and others. One of the main characteristics of PZT (Lead Zirconate Titanate), that allows to use it as sensor and actuator, is the piezoelectric effect, where the application of an electric field results in deformation of the material (inverse effect), while the application of mechanical tension results in the appearance of an electric field (direct effect). These characteristics associated to the concept of electric impedance and the concept of metric fault allow the location and the monitoring of the fault. This technique uses high frequencies and low voltage and it excites local modes, providing, the monitoring of any change on the structural mechanical impedance in the area of influence of the PZT. Artificial Neural Networks (ANN) are part of a wide concept called artificial intelligence. Neural networks has its base associated to the operation of the human brain, that after training possess the capacity "to learn". This science is a study object in several research centers and, although it already has great application. The success of its use depends of the case and planner's certain keenness, once the process is still empiric and theories are still being formulated. Several conceptions of neural networks... (Complete abstract, click electronic address below). / Mestre

Orientador: José Alfredo Covolan Ulson / Banca: Rogério Andrade Flauzino / Banca: Mario Eduardo Bordon / Resumo: O sistema de distribuiçã de energia elétrica no Brasil é constituído, na grande maioria, de alimentadores aéreos na classe 15 kV sujeitos a muitos tipos de defeitos. Embora existam muitos trabalhos propondo técnicas para a identificação e a localização das faltas, a maioria deles foi desenvolvida objetivando as redes de transmissão pouco ramificadas. Em se tratando de redes muito ramificadas e extensas, características princiapais de redes de distribuição elétrica, esses métodos não apresentam alta confiabilidade e segurança em termos de detecção, dificultando a localização das faltas. Este trabalho tem como objetivo descrever o desenvolvimento de um sistema de medição de baixo custo voltado para a monitoração de alimentadores aéreosm de distribuição de energia elétrica, que possibilite a identificação e a localização de faltas bem como a avaliação da qualidade da energia elétrica fornecida. Para tal, uma rede de sensores sem fio no padrão IEEE 802.15.4 é utilizada para adquirir os dados de tensão e corrente de cada frase e, a partir do processamento dessas informações, possibilitar a identificação de um ramal sob falta e a determinação do seu tipo. Com a instação de mediadores em pontos onde a rede de distribuição se ramifica, o ramal sob falta pode ser localizado, reduzindo assim o tempo de desligamento e os custos de manutenção / Abstract: The system of eletrecity distribution in Brazil is made mostly of air handlers in the class 15 kV, subject to many kinds of defects. Although there are many papers proposing techniques for the identification and location of faults, most of them were developed aiming at the broadcast networks little branched. In terms of networks very extensive and branched, like in distribution networks, these methods have low reliability and safety in terms of detection, makind the location of faults difficult. This paper aims to describe the development of a measurment system focused on low-cost monitoring of overhead distribution feeders of electricity, allowing for the identification and location of faults and the quality of power supplied. For this purpose, a network of wireless sensors on the IEEE 208.15.4 is used for data acquisition of each phase voltage and current and from the processing of such information is possible to determine a faulted extension and identification of its type. With the installation of meters at points where the distribution networks branches, the faulted extension can be located, thereby reducing the shutdown time and maintenance costs / Mestre

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system.