Submitted in complete fulfillment for the degree of Doctor of Philosophy (Biotechnology), Durban University of Technology, Durban, South Africa, 2016. / Seasonal nitrification breakdown is a major problem in wastewater treatment plants which makes it difficult for the plant operators to meet discharge limits. The present study focused on understanding the seasonal impact of environmental and operational parameters on nitrifiers and nitrification, in a biological nutrient removal wastewater treatment works situated in the midlands of KwaZulu Natal. Composite sludge samples (from the aeration tank), influent and effluent water samples were collected twice a month for 237 days. A combination of fluorescent in-situ hybridization, polymerase chain reaction (PCR)-clone library, quantitative polymerase chain reaction (qPCR) were employed for characterizing and quantifying the dominant nitrifiers in the plant. In order to have more insight into the activated sludge community structure, pyrosequencing was used in profiling the amoA locus of ammonia oxidizing bacteria (AOB) community whilst Illumina sequencing was used in characterising the plant’s total bacterial community. The nonlinear effect of operating parameters and environmental conditions on nitrification was also investigated using an adaptive neuro-fuzzy inference system (ANFIS), Pearson’s correlation coefficient and quadratic models. The plant operated with higher MLSS of 6157±783 mg/L during the first phase (winter) whilst it was 4728±1282 mg/L in summer. The temperature recorded in the aeration tanks ranged from 14.2oC to 25.1oC during the period. The average ammonia removal during winter was 60.0±18% whereas it was 83±13% during summer and this was found to correlate with temperature (r = 0.7671; P = 0.0008). A significant correlation was also found between the AOB (amoA gene) copy numbers and temperature in the reactors (α= 0.05; P=0.05), with the lowest AOB abundance recorded during winter. Sanger sequencing analysis indicated that the dominant nitrifiers were Nitrosomonas spp. Nitrobacter spp. and Nitrospira spp. Pyrosequencing revealed significant differences in the AOB population which was 6 times higher during summer compared to winter. The AOB sequences related to uncultured bacterium and uncultured AOB also showed an increase of 133% and 360% respectively when the season changed from winter to summer. This study suggests that vast population of novel, ecologically significant AOB species, which remain unexploited, still inhabit the complex activated sludge communities. Based on ANFIS model, AOB increased during summer season, when temperature was 1.4-fold higher than winter (r 0.517, p 0.048), and HRT decreased by 31% as a result of rainfall (r - 0.741, p 0.002). Food: microorganism ratio (F/M) and HRT formed the optimal combination of two inputs affecting the plant’s specific nitrification (qN), and their quadratic equation showed r2-value of 0.50. This study has significantly contributed towards understanding the complex relationship between the microbial population dynamics, wastewater composition and nitrification performance in a full-scale treatment plant situated in the subtropical region. This is the first study applying ANFIS technique to describe the nitrification performance at a full-scale WWTP, subjected to dynamic operational parameters. The study also demonstrated the successful application of ANFIS for determining and ranking the impact of various operating parameters on plant’s nitrification performance, which could not be achieved by the conventional spearman correlation due to the non-linearity of the interactions during wastewater treatment. Moreover, this study also represents the first-time amoA gene targeted pyrosequencing of AOB in a full-scale activated sludge is being done. / D

[pt] Esta tese apresenta uma nova metodologia para valoração dinâmica do Capital Intelectual, aplicada ao Capital Humano. Trata-se de oferecer, ao tomador de decisão, uma ferramenta capaz de calcular e comparar o retorno do investimento em ativos intangíveis, como ocorre com outros ativos tangíveis. Através da metodologia proposta, denominada KVA-ACHE, é possível estimar a quantidade potencial de conhecimento humano, utilizado na geração do resultado financeiro da empresa. Essa metodologia também permite medir variações de desempenho nos processos-chave que compõem a cadeia de valor da empresa e o impacto do investimento em educação em um determinado processo. O método KVA-ACHE é composto de cinco módulos, que são executados em três fases. Na primeira fase se avalia a empresa de forma agregada, segundo seu modelo estratégico e, na segunda fase, avalia-se a quantidade de conhecimento potencial e disponível, associado a cada processo-chave. A terceira fase é aplicado o método KVA e obtido o indicador de desempenho ROI. Ao final da sua aplicação, essa metodologia permite: identificar os processos que estão drenando resultado da empresa, através da observação de indicador financeiro adaptado, como o ROIK (Return on Investment on Knowledg), identificar a necessidade individualizada de treinamento para se atingir o máximo de desempenho em um determinado processochave; analisar o impacto percebido em termos percentuais do investimento em educação, realizado em determinado processo-chave; e, finalmente, dar uma visão sobre os recursos de conhecimentos e habilidades disponíveis na equipe de colaboradores, os quais poderão ser aproveitados na avaliação de novos negócios e desafios para empresa. A principal inovação dessa metodologia está no fato de se utilizar a Teoria dos Conjuntos Fuzzy e de Sistemas de Inferência Fuzzy - SIF para transformar conceitos relacionados à disponibilidade e ao uso de conhecimento humano em valores que, dessa forma, permitem a comparação de ativos intangíveis com ativos tangíveis. / [en] This thesis presents a new methodology for dynamic valuation of Intellectual Capital, applied to the Human Capital. It offers, to the decision-maker, a computational tool able to quote and compare the return on investment in intangible assets, as with tangible assets. Through the proposed methodology, called KVAACHE, it is possible to estimate the potential amount of human knowledge, used in generating the company’s financial results. This approach also allows the measurement of variations in performance in the key processes that make up the value chain of the company and the impact of investment in education in a given process. The method KVA-ACHE is composed of five modules, which are executed in three phases. The first phase evaluates the company on an aggregate basis, according to its strategic model, and, in the second phase, the amount of potential and available knowledge, associated with each key process, is evaluated. The third phase applies KVA method. This methodology allows: the identification of the processes that are draining the company’s income by looking at the adapted financial indicators, such as ROIK (Return on Investment on Knowledge); the individualized need for training to achieve maximum performance in a particular key process; the analysis of the impact noticed in terms of percentage of the investment in education, held in a certain key process; and finally, an insight into the resources of knowledge and skills available in the team of collaborators, which may be used in the assessment of new challenges and business to the enterprise. The main innovation of this methodology lies in the use of Fuzzy Set Theory and Fuzzy Inference Systems - FIS to transform concepts related to the availability and use of human knowledge into values, and thus allow the comparison of intangible assets with tangible assets.

Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au Vietnam. / Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam

[pt] Uma forma de avaliar a qualidade da água e a qualidade ambiental de um reservatório para monitoramento futuro é listar e analisar as concentrações de tudo o que a mesma tem. Tal lista poderia ser tão longa quanto o número de elementos analisados, podendo ir de 20 e poucos componentes comuns a centenas. É assim que vários índices de qualidade têm sido propostos por serem capazes de sintetizar o maior número destes parâmetros de qualidade em um único valor de fácil interpretação. Não obstante, uma vez que a maior parte dos índices formulados serem para águas moventes, os mesmos têm pouca utilidade para lagos e reservatórios. Lagos e reservatórios são geralmente avaliados e classificados com base em índices de estado trófico e em análises de suas composições químicas. Porém, um índice de estado trófico não tem a mesma representatividade de um índice de qualidade, visto que o termo qualidade sugere uma avaliação subjetiva, importante ressaltar essa distinção de conceitos. Excelente ou pobre, a referência de qualidade da água depende do seu uso e das atitudes locais das pessoas. A definição de estado trófico e seu índice correspondente deveriam permanecer neutros a tais julgamentos subjetivos, mantendo-se numa estrutura dentro da qual podem ser feitas várias avaliações da qualidade da água. Dessa forma, no presente trabalho, criou-se um modelo de avaliação da qualidade da água e da qualidade ambiental para um reservatório tropical oligo-mesotrófico (reservatório das Lajes) capaz de representar em uma escala numérica as gradações nos níveis de qualidade, além de levar em consideração a subjetividade implícita no conceito de qualidade. A subjetividade da avaliação em discussão motivou o emprego da Lógica Fuzzy, metodologia capaz de representar, de forma mais eficiente e clara, os limites dos intervalos de variação dos parâmetros de qualidade para um conjunto de categorias subjetivas, quando esses limites não são bem definidos ou são imprecisos. Assim, foi desenvolvida uma ferramenta computacional baseada em Sistemas de Inferência Fuzzy que avalia automaticamente a qualidade em função de variáveis físicas, químicas e biológicas do reservatório. O referido modelo foi desenvolvido com base no conhecimento de especialistas em qualidade de água e qualidade ambiental do Centro de Ciências Biológicas e da Saúde da Universidade Federal do Estado do Rio de Janeiro (UNIRIO) e do Departamento de Biologia Animal da Universidade Federal Rural do Rio de Janeiro (UFRRJ). O modelo foi avaliado utilizando dados de coleta do reservatório das Lajes coletados no ano 2005, 2008 e 2009. / [en] There are many approaches to monitor the water and environmental qualities of a reservoir. One approach is to list and analyze the concentration of chemicals and physical characteristics that the amount of water it contains. Such a list could be as long as the number of elements analyzed, from a few common components to hundreds. Thus, many indices have been proposed since they are able to synthesize as many of these quality parameters into a single value for an easy interpretation. However, majority of the indices are formulated to evaluate lentic ecosystems, they have little use for lakes and reservoirs. Lakes and reservoirs are generally evaluated and classified based on trophic state indices and chemical composition analysis. Nevertheless, a trophic state index does not have the same representativeness of a quality index. The term quality implies a subjective judgment that is best kept separate from the concept of trophic state. Excellent or poor, water quality depends on the use of that water and the local attitudes of the people. The definition of trophic state and its corresponding index should remain neutral to these subjective judgments, remaining a framework within which various evaluations of water quality may be made. Accordingly, in today s world of technology and advancement there exists a unique model to evaluate water quality and environmental quality for a tropical oligo-mesotrophic reservoir which is located and known as the reservoir of Lajes in the State of Rio de Janeiro, Brazil. This model is capable of representing quality levels on a numerical scale gradation, and also takes into consideration the subjectivity implicit in the concept of quality. The subjectivity, implicit in the concept of quality, motivated the use of fuzzy logic. This is a methodology to represent more efficiently the limits of ranges of quality parameters for a set of subjective categories, when these limits are not well defined or are inaccurate. As a result, we developed a computational tool based on a Fuzzy Inference System that automatically assesses the quality in terms of the physical, chemical and biological characteristics of the reservoir. The model was developed based on the knowledge of experts on water quality and environmental quality from the Biological Sciences and Health Center of Universidade Federal do Estado do Rio de Janeiro (UNIRIO) and from the Department of Animal Biology of the Universidade Federal Rural do Rio de Janeiro (UFRRJ). The model was evaluated with data from the Lajes reservoir during the years 2005, 2008 and 2009.

Made available in DSpace on 2016-06-02T19:03:58Z (GMT). No. of bitstreams: 1 5694.pdf: 2329501 bytes, checksum: 90a80d78f180e25fc719ec410704ff8f (MD5) Previous issue date: 2013-12-13 / Financiadora de Estudos e Projetos / In Computer Science, ontologies are used for knowledge representation in a number of applications, aiming to structure and handle domain semantics through models shared by humans and computational systems. Although traditional ontologies model semantic information and support reasoning tasks, they are based on a formalism which is less suitable to express the vagueness inherent in real-world phenomena and human language. To address this issue, many proposals investigate how traditional ontologies can be extended by incorporating concepts from fuzzy sets and fuzzy logic, resulting in fuzzy ontologies. In special, combining the formalism from fuzzy ontologies with fuzzy rule-based reasoning, which has been successfully applied in the context of fuzzy inference systems, can lead to more expressive inferences involving imprecision. In this sense, this doctoral thesis aims at exploring the integration of fuzzy ontology reasoning with fuzzy inference systems, resulting in the definition and the development of two approaches: HyFOM (Hybrid integration of Fuzzy Ontology and Mamdani reasoning) and FT-FIS (Fuzzy Tableau and Fuzzy Inference System). HyFOM is based on a hybrid architecture combining reasoners for ontologies, fuzzy ontologies and fuzzy inference systems, focusing on the interaction among its independent components. FT-FIS defines an interface between a fuzzy tableau-based algorithm and a fuzzy inference system, including the fuzzyRuleReasoning predicate that allows fuzzy rule-based reasoning to be invoked whenever necessary for fuzzy ontology reasoning tasks. The main contribution of HyFOM and FT-FIS comes from their reasoning architectures, which combine flexibility in terms of fuzzy rule semantics with the collaboration between inferences from both types of reasoning. Experiments regarding the recommendation of touristic attractions, based on synthetic data, revealed that HyFOM and FT-FIS provide integrated inferences, in addition to a more expressive approximation of the relation defined by fuzzy rules than the results from the fuzzyDL reasoner. In experiments involving the evaluation of chemical risk in food samples, based on real data, results obtained by HyFOM and FT-FIS are also more precise than fuzzyDL results, in comparison with reference values available in this domain. / No contexto da Ciência da Computação, ontologias são utilizadas para representação de conhecimento em diversas aplicações, com o intuito de estruturar e tratar a semântica de domínios específicos. Embora representem e permitam inferir conhecimento implícito, as ontologias convencionais baseiam-se em um formalismo que não é capaz de expressar a imprecisão presente em fenômenos do mundo real e na linguagem humana. Para abordar esta limitação, há diversas pesquisas que investigam a incorporação de conceitos da teoria de conjuntos fuzzy e da lógica fuzzy em ontologias, resultando em ontologias fuzzy. Em especial, combinar o formalismo das ontologias fuzzy com o raciocínio baseado em regras fuzzy, utilizado com sucesso no contexto de sistemas de inferência fuzzy, pode proporcionar uma maior expressividade com relação às inferências envolvendo imprecisão. Neste sentido, o objetivo deste projeto de doutorado é explorar a integração do raciocínio de ontologias fuzzy e de sistemas de inferência fuzzy, resultando na definição e no desenvolvimento das abordagens HyFOM (Hybrid integration of Fuzzy Ontology and Mamdani reasoning) e FT-FIS (Fuzzy Tableau and Fuzzy Inference System). HyFOM baseia-se em uma arquitetura híbrida que combina motores de inferência existentes na literatura para ontologias, ontologias fuzzy e sistemas de inferência fuzzy, com foco na interação entre seus componentes independentes. FT-FIS define uma interface entre um algoritmo baseado em tableau fuzzy e um sistema de inferência fuzzy, incluindo o predicado fuzzyRuleReasoning que permite invocar o raciocínio baseado em regras fuzzy quando for necessário para as tarefas de raciocínio da ontologia fuzzy. A principal contribuição das arquiteturas de raciocínio de HyFOM e FT-FIS está na combinação de flexibilidade, em termos da semântica das regras fuzzy, com a colaboração entre as inferências de ambos tipos de raciocínio. Experimentos considerando a recomendação de atrações turísticas, baseados em dados sintéticos, revelaram que HyFOM e FT-FIS são capazes de proporcionar inferências integradas, além de uma aproximação mais expressiva da relação estabelecida pelas regras fuzzy que os resultados providos pelo raciocinador fuzzyDL. Em experimentos envolvendo o domínio de risco químico em alimentos, baseado em dados reais, os resultados de HyFOM e FT-FIS também são mais precisos que os resultados de fuzzyDL, em comparação com valores de referência disponíveis nesse domínio.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-24T12:12:06Z No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 1937921 bytes, checksum: 0472ffffb70cabf120dc5de86d6626b1 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-24T16:55:00Z (GMT) No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 1937921 bytes, checksum: 0472ffffb70cabf120dc5de86d6626b1 (MD5) / Made available in DSpace on 2017-04-24T16:55:00Z (GMT). No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 1937921 bytes, checksum: 0472ffffb70cabf120dc5de86d6626b1 (MD5) Previous issue date: 2011-08-30 / A presente dissertação tem como objetivo discutir o uso de técnicas de otimização baseadas no gradiente conjugado e de informações de segunda ordem para o treinamento de sistemas de inferência fuzzy singleton e non-singleton. Além disso, as soluções computacionais derivadas são aplicadas aos problemas de classificação de distúrbios múltiplos e isolados em sinais elétricos. Os resultados computacionais, obtidos a partir de dados sintéticos de distúrbios em sinais de tensão, indicam que os sistemas de inferência fuzzy singleton e non-singleton treinados pelos algoritmos de otimização considerados apresentam maior velocidade de convergência e melhores taxas de classificação quando comparados com aqueles treinados pelo algoritmo de otimização baseada em informações de primeira ordem e é bastante competitivo em relação à rede neural artificial perceptron multicamadas - multilayer perceptron (MLP) e ao classificador de Bayes. / This master dissertation aims to discuss the use of optimization techniques based on the conjugated gradient and on second order information for the training of singleton or non-singleton fuzzy inference systems. In addition, the computacional solutions obtained are applied to isolated a multiple disturbances classification problems in electric signals. Computational results obtained from synthetic data from disturbances in electric signals indicate that singleton or non-singleton fuzzy inference systems trained by the considered optimization algorithms present greater convergence speed and better classification rates when compared to those data trained by an optimization algorithm based on first order information and is quite competitive with multilayer perceptron neural network and Bayesian classifier.

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.

Les travaux de cette thèse visent à apporter des éléments de solutions au problème de la durée de vie des systèmes pile à combustible (FCS – Fuel Cell System) de type à « membrane échangeuse de protons » (PEM – Proton Exchange Membrane) et se décline sur deux champs disciplinaires complémentaires :Une première approche vise à augmenter la durée de vie de celle-ci par la conception et la mise en œuvre d'une architecture de pronostic et de gestion de l'état de santé (PHM – Prognostics & Health Management). Les PEM-FCS, de par leur technologie, sont par essence des systèmes multi-physiques (électriques, fluidiques, électrochimiques, thermiques, mécaniques, etc.) et multi-échelles (de temps et d'espace) dont les comportements sont difficilement appréhendables. La nature non linéaire des phénomènes, le caractère réversible ou non des dégradations, et les interactions entre composants rendent effectivement difficile une étape de modélisation des défaillances. De plus, le manque d'homogénéité (actuel) dans le processus de fabrication rend difficile la caractérisation statistique de leur comportement. Le déploiement d'une solution PHM permettrait en effet d'anticiper et d'éviter les défaillances, d'évaluer l'état de santé, d'estimer le temps de vie résiduel du système, et finalement, d'envisager des actions de maîtrise (contrôle et/ou maintenance) pour assurer la continuité de fonctionnement. Une deuxième approche propose d'avoir recours à une hybridation passive de la PEMFC avec des super-condensateurs (UC – Ultra Capacitor) de façon à faire fonctionner la pile au plus proche de ses conditions opératoires optimales et ainsi, à minimiser l'impact du vieillissement. Les UCs apparaissent comme une source complémentaire à la PEMFC en raison de leur forte densité de puissance, de leur capacité de charge/décharge rapide, de leur réversibilité et de leur grande durée de vie. Si l'on prend l'exemple des véhicules à pile à combustible, l'association entre une PEMFC et des UCs peut être réalisée en utilisant un système hybride de type actif ou passif. Le comportement global du système dépend à la fois du choix de l'architecture et du positionnement de ces éléments en lien avec la charge électrique. Aujourd'hui, les recherches dans ce domaine se focalisent essentiellement sur la gestion d'énergie entre les sources et stockeurs embarqués ; et sur la définition et l'optimisation d'une interface électronique de puissance destinée à conditionner le flux d'énergie entre eux. Cependant, la présence de convertisseurs statiques augmente les sources de défaillances et pannes (défaillance des interrupteurs du convertisseur statique lui-même, impact des oscillations de courant haute fréquence sur le vieillissement de la pile), et augmente également les pertes énergétiques du système complet (même si le rendement du convertisseur statique est élevé, il dégrade néanmoins le bilan global). / This thesis work aims to provide solutions for the limited lifetime of Proton Exchange Membrane Fuel Cell Systems (PEM-FCS) based on two complementary disciplines:A first approach consists in increasing the lifetime of the PEM-FCS by designing and implementing a Prognostics & Health Management (PHM) architecture. The PEM-FCS are essentially multi-physical systems (electrical, fluid, electrochemical, thermal, mechanical, etc.) and multi-scale (time and space), thus its behaviors are hardly understandable. The nonlinear nature of phenomena, the reversibility or not of degradations and the interactions between components makes it quite difficult to have a failure modeling stage. Moreover, the lack of homogeneity (actual) in the manufacturing process makes it difficult for statistical characterization of their behavior. The deployment of a PHM solution would indeed anticipate and avoid failures, assess the state of health, estimate the Remaining Useful Lifetime (RUL) of the system and finally consider control actions (control and/or maintenance) to ensure operation continuity.A second approach proposes to use a passive hybridization of the PEMFC with Ultra Capacitors (UC) to operate the fuel cell closer to its optimum operating conditions and thereby minimize the impact of aging. The UC appear as an additional source to the PEMFC due to their high power density, their capacity to charge/discharge rapidly, their reversibility and their long life. If we take the example of fuel cell hybrid electrical vehicles, the association between a PEMFC and UC can be performed using a hybrid of active or passive type system. The overall behavior of the system depends on both, the choice of the architecture and the positioning of these elements in connection with the electric charge. Today, research in this area focuses mainly on energy management between the sources and embedded storage and the definition and optimization of a power electronic interface designated to adjust the flow of energy between them. However, the presence of power converters increases the source of faults and failures (failure of the switches of the power converter and the impact of high frequency current oscillations on the aging of the PEMFC), and also increases the energy losses of the entire system (even if the performance of the power converter is high, it nevertheless degrades the overall system).