Au cours des deux dernières décennies, la prise de conscience du changement climatique et des conséquences du réchauffement climatique a incité diverses institutions à prendre de nouvelles directives. Ces directives portent principalement sur le contrôle des émissions des gaz à effet de serre, sur l'utilisation des ressources énergétiques non conventionnelles et l'optimisation de la consommation d'énergie dans les systèmes existants. L'Union européenne a proposé de nombreux projets dans le cadre du 7e PCRD pour réaliser jusqu'à 20% d’économies d'énergie d’ici 2020. En particulier, selon la directive sur l'efficacité énergétique, les bâtiments sont majoritairement responsables de 40% des dépenses énergétiques en Europe et de 36% des émissions de CO2 ; c’est la raison pour laquelle un ensemble d’initiatives européennes dans le cadre du 7ième PCRD favorise l'utilisation de technologie intelligente dans les bâtiments et rationalise les règles existantes. Energy IN TIME est l'un des projets axés sur l'élaboration d'une méthode de contrôle basée sur la simulation intelligente de l'énergie qui permettra de réduire la consommation des bâtiments non résidentiels. Ce mémoire de thèse propose plusieurs solutions novatrices pour réaliser les objectifs du projet mandaté à l'Université de Lorraine. Les solutions développées dans le cadre de ce projet devraient être validées sur différents sites européens de démonstration. Une première partie présente l'analyse détaillée de ces sites de démonstration et leurs contraintes respectives. Un cadre général correspondant à la construction type de ces sites a été élaboré pour simuler leur comportement. Ce cadre de construction de référence sert de banc d'essai pour la validation des solutions proposées dans ce travail de thèse. Sur la base de la conception de la structure de construction de référence, nous présentons une formulation de contrôle économique utilisant un modèle de contrôle prédictif minimisant la consommation d'énergie. Ce contrôle optimal possède des propriétés de contrôle conscientes de la maintenance. En outre, comme les bâtiments sont des systèmes complexes, les occurrences de pannes peuvent entraîner une détérioration de l'efficacité énergétique ainsi que du confort thermique pour les occupants à l'intérieur des bâtiments. Pour résoudre ce problème, nous avons élaboré une stratégie de diagnostic des dysfonctionnements et une stratégie de contrôle adaptatif des défauts basé sur le modèle économique ; les résultats en simulation ont été obtenus sur le bâtiment de référence. En outre, l'application des solutions proposées peut permettre de relever des défis ambitieux en particulier dans le cas de bâtiments à grande échelle. Dans la partie finale de cette thèse, nous nous concentrons sur le contrôle économique des bâtiments à grande échelle en formulant une approche novatrice du contrôle prédictif de mode réparti. Cette formule de contrôle distribué présente de nombreux avantages tels que l'atténuation de la propagation des défauts, la flexibilité dans la maintenance du bâtiment et les stratégies simplifiées de contrôle du plug-and-play. Enfin, une attention particulière est accordée au problème d'estimation des mesures dont le nombre est limité sur des bâtiments à grande échelle. Les techniques d'estimation avancées proposées sont basées sur les méthodologies de l'horizon mobile. Leur efficacité est démontrée sur les systèmes de construction de référence / Since the last two decades, there has been a growing awareness about the climate change and global warming that has instigated several Directorate initiatives from various administrations. These initiatives mainly deal with controlling greenhouse gas emissions, use of non-conventional energy resources and optimization of energy consumption in the existing systems. The European Union has proposed numerous projects under FP7 framework to achieve the energy savings up to 20% by the year 2020. Especially, stated by the Energy Efficiency Directive, buildings are majorly responsible for 40% of energy resources in Europe and 36% of CO2 emission. Hence a class of projects in the FP7 framework promotes the use of smart technology in the buildings and the streamline existing rules. Energy IN TIME is one of the projects focused on developing a Smart Energy Simulation Based Control method which will reduce the energy consumption in the operational stage of existing non-residential buildings. Essentially, this thesis proposes several novel solutions to fulfill the project objectives assigned to the University of Lorraine. The developed solutions under this project should be validated on the demonstration sites from various European locations. We design a general benchmark building framework to emulate the behavior of demonstration sites. This benchmark building framework serves as a test bench for the validation of proposed solutions given in this thesis work. Based on the design of benchmark building layout, we present an economic control formulation using model predictive control minimizing the energy consumption. This optimal control has maintenance-aware control properties. Furthermore, as in buildings, fault occurrences may result in deteriorating the energy efficiency as well as the thermal comfort for the occupants inside the buildings. To address this issue, we design a fault diagnosis and fault adaptive control techniques based on the model predictive control and demonstrate the simulation results on the benchmark building. Moreover, the application of these proposed solutions may face great challenges in case of large-scale buildings. Therefore, in the final part of this thesis, we concentrate on the economic control of large-scale buildings by formulating a novel approach of distributed model predictive control. This distributed control formulation holds numerous advantages such as fault propagation mitigation, flexibility in the building maintenance and simplified plug-and-play control strategies, etc... Finally, a particular attention is paid to the estimation problem under limited measurements in large-scale buildings. The suggested advanced estimation techniques are based on the moving horizon methodologies and are demonstrated on the benchmark building systems

Dans cette thèse, le diagnostic d'un système non linéaire a été réalisé par une analyse de données. Initialement conçue pour analyser les données liées par des relations linéaires, l'Analyse en Composantes Principales (ACP) est couplée aux méthodes à noyau pour détecter, localiser et estimer l'amplitude des défauts sur des systèmes non linéaires. L'ACP à noyau consiste à projeter les données par l'intermédiaire d'une application non linéaire dans un espace de dimension élevée dénommé espace des caractéristiques où l'ACP linéaire est appliquée. Ayant fait la projection à l'aide de noyaux, la détection peut facilement être réalisée dans l'espace des caractéristiques. Cependant, l'estimation de l'amplitude du défaut nécessite la résolution d'un problème d'optimisation non linéaire. Une étude de contributions permet de localiser et d'estimer ces amplitudes. La variable ayant la plus grande contribution est susceptible d'être affectée par un défaut. Dans notre travail, nous avons proposé de nouvelles méthodes pour les phases de localisation et d'estimation des défauts pour lesquelles les travaux existants ont des limites. La nouvelle méthode proposée est basée sur les contributions sous contraintes permettant d'obtenir une reconstruction parcimonieuse des variables. L'efficacité des méthodes proposées est montrée sur un réacteur à agitation continue (CSTR). / In this thesis, the diagnosis of a nonlinear system was performed using data analysis. Initially developed to analyze linear system, Principal Component Analysis (PCA) is coupled with kernel methods for detection, isolation and estimation of faults' magnitude for nonlinear systems. Kernel PCA consists in projecting data using a nonlinear mapping function into a higher dimensional space called feature space where the linear PCA is applied. Due to the fact that the projections are done using kernels, the detection can be performed in the feature space. However, estimating the magnitude of the fault requires the resolution of a nonlinear optimization problem. The variables' contributions make it possible to isolate and estimate these magnitudes. The variable with the largest contribution may be considered as faulty. In our work, we proposed new methods for the isolation and estimation phases for which previous work has some limitations. The new proposed method in this thesis is based on contributions under constraints. The effectiveness of the developed methods is illustrated on the simulated continuous stirred tank reactor (CSTR).

More electric aircraft is an electrification scheme of aircraft system with high technical feasibility and good economy. It can reduce the weight of aircraft structure, improve maintenance efficiency and reduce fire hazards. However, the electrification of aircraft system will drastically increase the proportion of electrical equipment, the total power demand and the difficulty of fault diagnosis. This paper uses the energy management method to take up the challenge, with focus on fault diagnosis of permanent-magnet synchronous machines (PMSMs), adaptive load shedding and energy efficient aircraft design. A literature review of the concept evolution from all/more-electric aircraft to energy-optimized aircraft is presented. The main issues of the aircraft electrification process are summarized, and followed by an introduction to the current research and methods. The model of the aircraft electrical system is qualitatively and mathematically recalled, including the generator, the battery, the DC motor, the AC motor, and the electric power converter. The accuracy and computation cost of the aircraft model depends on the complexity of the subsystem models that are involved. Therefore, the level of detail that is necessary for a good precision-versus-simulation-time ratio is discussed by taking the electric system of an industrial level hybrid energy quadcoptor UAV as an example. The analysis shows that the bi-directional instruments, i.e. the electric machine, should be modeled in details while other components can be simplified. PMSMs are a group of on-board electric machines with promising future prospects because of high power density and stability. The model of PMSMs is further developed in this work, especially in the inter-turn and phase-to-phase short-circuit conditions. In case of inter-turn short-circuit fault, a winding-function-based and a fault-current-based model are separately developed. The accuracy of both models are verified and compared through experimental results. The fault-current-based modeling method is applied to the phase-to-phase short-circuit fault and experimentally examined and discussed. General condition monitoring methods require the use of a large number of sensors. A fault detection and isolation method that can have low requirement of sensor is recalled and inherited. The description of the fault phase identification index using this method is relatively imprecise, which is not applicable to the inter-turn short-circuit fault. In this work, the analytical expression of the faulty phase identification index is derived based on the fault models. A method to isolate inter-turn and phase-to-phase short-circuit faults is proposed by a combination of the current- and the voltage-signature residuals. This development expands the application scope of the original fault detection and isolation tool and improves its accuracy. The validity of this fault diagnosis method has been verified by experimental results.Load management is developed to guarantee the normal operation of critical loads by shedding some other loads in case of emergency. Generally, binary decisions are made: either something has gone wrong or everything is fine. However, different types of fault influence the working performance of the load and the entire network in different ways. There are multiple states between totally wrong and pure fine, and the load management decision should be adaptive to each state. In this work, fuzzy logic method is used to degrade the load priority according to the instantaneous working state. Combining it with the fault detection and isolation process, a fault-tolerant adaptive load management is achieved. Finally, this work discusses the aircraft design from the energy management point of view, which consists of the energy efficiency analysis and the multidisciplinary energy efficient design of the integrated aircraft system. The first thermodynamic efficiency has been widely used as a common parameter for depicting the energy utilization, i.e. the ratio of output to input power of the system. However, it ignores the irreversible increase of the entropy and cannot reveal the upper limit of the available work of the system.Based on the second thermodynamic law, this work uses the exergy parameters to analyze the energy utilization of a MEA design scheme. Based on the exergy analysis, an energy-efficient aircraft design method is proposed by optimizing the exergy lost of the whole design. The method could provide a global optimization reference for the integrated aircraft design of a MEA. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro / A análise de gases dissolvidos tem sido aplicada há décadas como a principal técnica de manutenção preditiva para diagnosticar defeitos incipientes em transformadores de potência, tendo em vista que a decomposição do óleo mineral isolante produz gases que permanecem dissolvidos na fase líquida. Entretanto, apesar da importância desta técnica, os métodos de diagnóstico mais conhecidos são baseados em constatações de modelos termodinâmicos e composicionais simplificados para a decomposição térmica do óleo mineral isolante, em conjunto com dados empíricos. Os resultados de simulação obtidos a partir desses modelos não reproduzem satisfatoriamente os dados empíricos. Este trabalho propõe um modelo termodinâmico flexível aprimorado para mimetizar o efeito da cinética de formação de sólidos como restrição ao equilíbrio e seleciona, entre quatro modelos composicionais, aquele que apresenta o melhor desempenho na simulação da decomposição térmica do óleo mineral isolante. Os resultados de simulação obtidos a partir do modelo proposto apresentaram uma melhor adequação a dados empíricos do que aqueles obtidos a partir dos modelos clássicos. O modelo propostofoi, ainda, aplicado ao desenvolvimento de um método de diagnóstico com base fenomenológica.Os desempenhos desta nova proposta fenomenológica e de métodos clássicos de diagnóstico por análise de gases dissolvidos foram comparados e discutidos; o método proposto alcançou desempenho superior a vários métodos usualmente empregados nessa área do conhecimento. E, ainda, um procedimento geral para a aplicação do novo método de diagnóstico é descrito / The dissolved gas analysis has been applied for decades as the main predictive maintenance technique for diagnosing incipient faults in power transformers since the decomposition of the mineral insulating oil produces gases that remain dissolved in the liquid phase. Nevertheless, the most known diagnostic methods are based on findings of simplified thermodynamic and compositional models for the thermal decomposition of mineral insulating oil, in addition to empirical data. The simulations results obtained from these models do not satisfactorily reproduce the empirical data. This work proposes a flexible thermodynamic model enhanced to mimic the kinetic effect of solid formation as a restriction to equilibrium and selects, among four compositional models, the one offering the best performance on the simulation of the thermal decomposition of mineral insulating oil. The simulation results obtained from the proposed model showed better adequacy to reported data than the results obtained from the classical models. The proposed model was also applied in the development of a diagnostic method with a phenomenological basis. The performances of this new phenomenological proposition and of classical dissolved gas analysis diagnostic methods are compared and discussed; the proposed method achieved a performance superior to several methods usually employed in this area of knowledge.Also, a general procedure for the application of the new diagnostic method is described

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Quando o sistema elÃtrico experimenta qualquer distÃrbio uma avalanche de alarmes à reportada ao Centro de OperaÃÃo do Sistema (COS) dificultando ao operador na identificaÃÃo da causa do distÃrbio. O grande volume de informaÃÃes disponibilizado pelos sistemas supervisÃrios em condiÃÃo de falta no sistema elÃtrico à de pouco valor se nÃo possibilitar um rÃpido diagnÃstico, para uma pronta e correta tomada de decisÃo e restabelecimento à condiÃÃo normal de operaÃÃo. Usando como entrada os dados informados pelo SCADA foi desenvolvido um Sistema de DiagnÃstico de Faltas (SDF), baseado em Redes de Petri Coloridas (RPC), que filtra as informaÃÃes do SCADA e à capaz de fornecer em tempo real aos operadores o diagnÃstico para as faltas no sistema. O diagnÃstico à obtido a partir da simulaÃÃo off-line de um expressivo nÃmero de possÃveis faltas no sistema em que a marcaÃÃo final da RPC para cada falta à convertida em diagnÃstico. Um Programa Especialista foi desenvolvido para a interpretaÃÃo da marcaÃÃo final fornecida pela RPC para geraÃÃo automÃtica do diagnÃstico de falta. O Programa Especialista interpreta os resultados do modelo RPC independente do sistema elÃtrico monitorado, diagnosticando os eventos que ocorrem tanto em uma subestaÃÃo quanto nas linhas de transmissÃo que ligam as subestaÃÃes, fornecendo um diagnÃstico rÃpido, sucinto, e com formato e linguagem comuns ao operador. / When a fault occurs in an electrical system often an avalanche of information is made available to the System Operation Center making it difficult to the operator to identify the cause of the fault. The great deal of information provided by the supervisory system is of any value if it does not make easy to the operator, to take a right and prompt decision to bring the system back to normal operation. Based on the SCADA information a Fault Diagnosis System (SDF) was developed, which uses the Colored Petri Nets (CPN) method to filter out the large amount of information made available by the SCADA system and then to give the fault diagnosis. The drawback of the SDF is that the fault diagnosis is developed off line from the CPN final markings for all the likely fault conditions on the power system. In this work an Expert Program is developed to automatically convert the CPN final markings into the system fault diagnosis. The Expert Program interprets the results of the CPN model independent of the monitored electrical system, it diagnosis events that occur in substations as much as the transmission lines that connect the substations, providing a fast and concise diagnosis with common format and language to the operator.

CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / O diagnÃstico de falhas incipientes em transformadores de potÃncia imersos em Ãleo està diretamente relacionado à avaliaÃÃo das condiÃÃes do sistema de isolamento. Este estudo aborda a relaÃÃo entre os gases dissolvidos no Ãleo e a qualidade do Ãleo mineral isolante utilizado em transformadores de potÃncia. As redes neurais artificiais sÃo utilizadas na abordagem da avaliaÃÃo das condiÃÃes operacionais do Ãleo isolante em transformadores de potÃncia, que à caracterizada por um comportamento dinÃmico nÃo-linear. As condiÃÃes de operaÃÃo e a integridade do sistema de isolamento de um transformador de potÃncia podem ser inferidas atravÃs das anÃlises fÃsico-quÃmicas e cromatogrÃficas (AnÃlise de GÃs Dissolvido). Estes ensaios permitem estabelecer procedimentos de operaÃÃo e manutenÃÃo do equipamento e normalmente sÃo realizados simultaneamente. Esta tese de doutorado propÃe um mÃtodo que pode ser usado para extrair informaÃÃes cromatogrÃficas usando as anÃlises fÃsico-quÃmicas atravÃs de redes neurais artificiais. As anÃlises atuais das propriedades fÃsico-quÃmicas fornecem apenas diagnÃstico do estado do Ãleo, o que nÃo permite o diagnÃstico de falhas incipientes. Acredita-se que, as concessionÃrias de energia podem melhorar a confiabilidade na previsÃo de falhas incipientes a um custo menor com este mÃtodo, uma vez que apenas um ensaio à necessÃrio. Os resultados mostraram que esta estratÃgia à promissora com mÃdia de acertos em diagnÃsticos de falhas maiores que 72%. O objetivo deste trabalho à a aplicaÃÃo direta do diagnÃstico de falhas incipientes atravÃs da utilizaÃÃo de propriedades fÃsico-quÃmicas, sem a necessidade de fazer uma cromatografia do Ãleo. / The diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. This search is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a nonlinear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA â Dissolved Gas Analysis) profiles of the isolating oil. This tests allow establishing procedures for operating and maintaining the equipment and usually are performed simultaneously. This work proposes a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. The present analysis of physicochemical properties only provide a diagnostic tool for the oil quality, which does not allow the diagnosis of incipient faults. ItÂs believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method, since only one test is required. The results show this strategy might be promising with an average accuracy for diagnosis of faults greater than 72%. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.

Aujourd'hui, les systèmes embarqués sont de plus en plus utilisés pour contrôler les systèmes complexes. Dans ce travail de thèse, nous nous intéressons aux systèmes embarqués critiques utilisés pour la commande de systèmes de transport comme les systèmes ferroviaires. Le but de ce travail est de permettre la conception de systèmes tolérants aux fautes pour le contrôle-commande des systèmes de transport. Nous proposons une nouvelle approche de modélisation des systèmes embarqués temporisés pour le diagnostic de leurs fautes. Elle est basée sur une décomposition structurelle du système et sur une extension de la diagnosticabilité modulaire au contexte des systèmes temporisés. On distingue deux approches de base pour le diagnostic de fautes des SED, une approche basée sur les diagnostiqueurs et une approche basée sur les signatures temporelles causales (STC). La principale limite de l’approche diagnostiqueur réside dans la gestion de l’explosion combinatoire. Dans ce travail, notre verrou principal est de combattre cette limite. Nous proposons une nouvelle méthode basée sur l’ingénierie par les modèles pour le diagnostic des systèmes embarqués critiques. D’autre part, la limite majeure de l’approche STC est la garantie de la cohérence d’une base de STC. Un deuxième niveau de difficulté réside dans l’interprétation des événements en entrée du système de diagnostic dans le cadre de l’hypothèse de défaillances multiples. Dans ce travail, nous proposons deux méthodes différentes pour la vérification de la cohérence d’une base de STC et nous proposons un algorithme d’interprétation basé sur le concept de monde qui garantit la correction du diagnostic / Today, embedded systems are increasingly used to control complex systems. In this thesis, we are interested in critical embedded systems used for the control of transport systems such as railway systems. The aim of this work is to enable the design of fault-tolerant systems for the control of transport systems. We propose a new timed embedded systems modeling approach to diagnose their faults. It is based on decomposition of the system and structural extension of diagnosability context of modular timed systems. In DES, there are two basic approaches for diagnosis: diagnoser based approach and chronicles (Causal Temporal Signature (CTS)) based approach. The major limitation of diagnoser approaches rely in the management of the combinatorial explosion related to the formalism of automata. In this work, our main lock is to combat this limit. We propose new engineering models based method for the diagnosis of critical embedded systems. On the other hand, the major limitation of chronicles approach is first to be able to guaranty the consistency of a database. A second level of difficulty is in interpreting some sequences of events at the input of the diagnostic system under the hypothesis of multiple failures. In this work, we propose two different methods to verify the consistency of a set of CTS and we propose an interpretation algorithm based on a concept of worlds which guarantees the correct diagnosis

In this thesis two different problems in distributed sensor networks are considered. Part I involves optimal quantiser design for decentralised estimation of a two-state hidden Markov model with dual sensors. The notion of optimality for quantiser design is based on minimising the probability of error in estimating the hidden Markov state. Equations for the filter error are derived for the continuous (unquantised) sensor outputs (signals), which are used to benchmark the performance of the quantisers. Minimising the probability of filter error to obtain the quantiser breakpoints is a difficult problem therefore an alternative method is employed. The quantiser breakpoints are obtained by maximising the mutual information between the quantised signals and the hidden Markov state. This method is known to work well for the single sensor case. Cases with independent and correlated noise across the signals are considered. The method is then applied to Markov processes with Gaussian signal noise, and further investigated through simulation studies. Simulations involving both independent and correlated noise across the sensors are performed and a number of interesting new theoretical results are obtained, particularly in the case of correlated noise. In Part II, the focus shifts to the detection of faults in helicopter transmission systems. The aim of the investigation is to determine whether the acoustic signature can be used for fault detection and diagnosis. To investigate this, statistical change detection algorithms are applied to acoustic vibration data obtained from the main rotor gearbox of a Bell 206 helicopter, which is run at high load under test conditions.

The present work aims to increase the diagnosis systems capabilities by choosing the location of sensors in the process. Therefore, appropriate sensor location will lead to better diagnosis performance and implementation easiness. The work is based on structural models ands some simplifications are considered in order to only focus on the sensor placement analysis. Several approaches are studied to solve the sensor placement problem. All of them find the optimal sensor configuration. The sensor placement techniques are applied to a fuel cell stack system. The model used to describe the behaviour of this system consists of non-linear equations. Furthermore, there are 30 candidate sensors to improve the diagnosis specifications. The results obtained from this case study are used to strength the applicability of the proposed approaches. / El present treball té per objectiu incrementar les prestacions dels diagnosticadors mitjançant la localització de sensors en el procés. D'aquesta manera, instal·lant els sensors apropiats s'obtenen millors diagnosticador i més facilitats d'implementació. El treball està basat en models estructurals i contempla una sèrie de simplificacions per tal de entrar-se només en la problemàtica de la localització de sensors. S'utilitzen diversos enfocs per tal de resoldre la localització de sensors, tot ells tenen com objectiu trobar la configuració òptima de sensors. Les tècniques de localització de sensors són aplicades a un sistema basat en una pila de combustible. El model d'aquest sistema està format per equacions no lineals. A més, hi ha la possibilitat d'instal·lar fins a 30 sensors per tal de millorar la diagnosis del sistema. Degut a aquestes característiques del sistema i del model, els resultats obtinguts mitjançant aquest cas d'estudi reafirmen l'aplicabilitat dels mètodes proposats.

The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.