Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Estudo avaliativo de um algoritmo genético auto-organizável e multiobjetivo utilizando aprendizado de máquina para aplicações de telecomunicações

This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

Dilema da diversidade-acurácia: um estudo empírico no contexto de multiclassificadores

Multi-classifier systems, also known as ensembles, have been widely used to solve several problems, because they, often, present better performance than the individual classifiers that form these systems. But, in order to do so, it s necessary that the base classifiers to be as accurate as diverse among themselves this is also known as diversity/accuracy dilemma. Given its importance, some works have investigate the ensembles behavior in context of this dilemma. However, the majority of them address homogenous ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this thesis, using genetic algorithms, performs a detailed study on the dilemma diversity/accuracy for heterogeneous ensembles

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm

Análise das medidas de boa e má diversidade na construção de comitês de classificadores através de metaheurísticas de otimização multiobjetivo

Committees of classifiers may be used to improve the accuracy of classification systems, in other words, different classifiers used to solve the same problem can be combined for creating a system of greater accuracy, called committees of classifiers. To that this to succeed is necessary that the classifiers make mistakes on different objects of the problem so that the errors of a classifier are ignored by the others correct classifiers when applying the method of combination of the committee. The characteristic of classifiers of err on different objects is called diversity. However, most measures of diversity could not describe this importance. Recently, were proposed two measures of the diversity (good and bad diversity) with the aim of helping to generate more accurate committees. This paper performs an experimental analysis of these measures applied directly on the building of the committees of classifiers. The method of construction adopted is modeled as a search problem by the set of characteristics of the databases of the problem and the best set of committee members in order to find the committee of classifiers to produce the most accurate classification. This problem is solved by metaheuristic optimization techniques, in their mono and multi-objective versions. Analyzes are performed to verify if use or add the measures of good diversity and bad diversity in the optimization objectives creates more accurate committees. Thus, the contribution of this study is to determine whether the measures of good diversity and bad diversity can be used in mono-objective and multi-objective optimization techniques as optimization objectives for building committees of classifiers more accurate than those built by the same process, but using only the accuracy classification as objective of optimization

Uma colônia de formigas para o caminho mais curto multiobjetivo

Multi-objective combinatorial optimization problems have peculiar characteristics that require optimization methods to adapt for this context. Since many of these problems are NP-Hard, the use of metaheuristics has grown over the last years. Particularly, many different approaches using Ant Colony Optimization (ACO) have been proposed. In this work, an ACO is proposed for the Multi-objective Shortest Path Problem, and is compared to two other optimizers found in the literature. A set of 18 instances from two distinct types of graphs are used, as well as a specific multiobjective performance assessment methodology. Initial experiments showed that the proposed algorithm is able to generate better approximation sets than the other optimizers for all instances. In the second part of this work, an experimental analysis is conducted, using several different multiobjective ACO proposals recently published and the same instances used in the first part. Results show each type of instance benefits a particular type of instance benefits a particular algorithmic approach. A new metaphor for the development of multiobjective ACOs is, then, proposed. Usually, ants share the same characteristics and only few works address multi-species approaches. This works proposes an approach where multi-species ants compete for food resources. Each specie has its own search strategy and different species do not access pheromone information of each other. As in nature, the successful ant populations are allowed to grow, whereas unsuccessful ones shrink. The approach introduced here shows to be able to inherit the behavior of strategies that are successful for different types of problems. Results of computational experiments are reported and show that the proposed approach is able to produce significantly better approximation sets than other methods

Distribuição de derivados de petróleo por redes de polidutos: uma abordagem através de algoritmos evolucionários híbridos para um problema triobjetivo

An important problem faced by the oil industry is to distribute multiple oil products through pipelines. Distribution is done in a network composed of refineries (source nodes), storage parks (intermediate nodes), and terminals (demand nodes) interconnected by a set of pipelines transporting oil and derivatives between adjacent areas. Constraints related to storage limits, delivery time, sources availability, sending and receiving limits, among others, must be satisfied. Some researchers deal with this problem under a discrete viewpoint in which the flow in the network is seen as batches sending. Usually, there is no separation device between batches of different products and the losses due to interfaces may be significant. Minimizing delivery time is a typical objective adopted by engineers when scheduling products sending in pipeline networks. However, costs incurred due to losses in interfaces cannot be disregarded. The cost also depends on pumping expenses, which are mostly due to the electricity cost. Since industrial electricity tariff varies over the day, pumping at different time periods have different cost. This work presents an experimental investigation of computational methods designed to deal with the problem of distributing oil derivatives in networks considering three minimization objectives simultaneously: delivery time, losses due to interfaces and electricity cost. The problem is NP-hard and is addressed with hybrid evolutionary algorithms. Hybridizations are mainly focused on Transgenetic Algorithms and classical multi-objective evolutionary algorithm architectures such as MOEA/D, NSGA2 and SPEA2. Three architectures named MOTA/D, NSTA and SPETA are applied to the problem. An experimental study compares the algorithms on thirty test cases. To analyse the results obtained with the algorithms Pareto-compliant quality indicators are used and the significance of the results evaluated with non-parametric statistical tests.

Algoritmos experimentais para o problema biobjetivo da árvore geradora quadrática em adjacência de arestas

The Quadratic Minimum Spanning Tree (QMST) problem is a generalization of the Minimum Spanning Tree problem in which, beyond linear costs associated to each edge, quadratic costs associated to each pair of edges must be considered. The quadratic costs are due to interaction costs between the edges. When interactions occur between adjacent edges only, the problem is named Adjacent Only Quadratic Minimum Spanning Tree (AQMST). Both QMST and AQMST are NP-hard and model a number of real world applications involving infrastructure networks design. Linear and quadratic costs are summed in the mono-objective versions of the problems. However, real world applications often deal with conflicting objectives. In those cases, considering linear and quadratic costs separately is more appropriate and multi-objective optimization provides a more realistic modelling. Exact and heuristic algorithms are investigated in this work for the Bi-objective Adjacent Only Quadratic Spanning Tree Problem. The following techniques are proposed: backtracking, branch-and-bound, Pareto Local Search, Greedy Randomized Adaptive Search Procedure, Simulated Annealing, NSGA-II, Transgenetic Algorithm, Particle Swarm Optimization and a hybridization of the Transgenetic Algorithm with the MOEA-D technique. Pareto compliant quality indicators are used to compare the algorithms on a set of benchmark instances proposed in literature.

Algoritmos experimentais para o problema biobjetivo da árvore geradora quadrática em adjacência de arestas

The Quadratic Minimum Spanning Tree (QMST) problem is a generalization of the Minimum Spanning Tree problem in which, beyond linear costs associated to each edge, quadratic costs associated to each pair of edges must be considered. The quadratic costs are due to interaction costs between the edges. When interactions occur between adjacent edges only, the problem is named Adjacent Only Quadratic Minimum Spanning Tree (AQMST). Both QMST and AQMST are NP-hard and model a number of real world applications involving infrastructure networks design. Linear and quadratic costs are summed in the mono-objective versions of the problems. However, real world applications often deal with conflicting objectives. In those cases, considering linear and quadratic costs separately is more appropriate and multi-objective optimization provides a more realistic modelling. Exact and heuristic algorithms are investigated in this work for the Bi-objective Adjacent Only Quadratic Spanning Tree Problem. The following techniques are proposed: backtracking, branch-and-bound, Pareto Local Search, Greedy Randomized Adaptive Search Procedure, Simulated Annealing, NSGA-II, Transgenetic Algorithm, Particle Swarm Optimization and a hybridization of the Transgenetic Algorithm with the MOEA-D technique. Pareto compliant quality indicators are used to compare the algorithms on a set of benchmark instances proposed in literature.

Investigações sobre técnicas de arquivamento para otimizadores multiobjetivo

Multi-objective problems may have many optimal solutions, which together form the Pareto optimal set. A class of heuristic algorithms for those problems, in this work called optimizers, produces approximations of this optimal set. The approximation set kept by the optmizer may be limited or unlimited. The benefit of using an unlimited archive is to guarantee that all the nondominated solutions generated in the process will be saved. However, due to the large number of solutions that can be generated, to keep an archive and compare frequently new solutions to the stored ones may demand a high computational cost. The alternative is to use a limited archive. The problem that emerges from this situation is the need of discarding nondominated solutions when the archive is full. Some techniques were proposed to handle this problem, but investigations show that none of them can surely prevent the deterioration of the archives. This work investigates a technique to be used together with the previously proposed ideas in the literature to deal with limited archives. The technique consists on keeping discarded solutions in a secondary archive, and periodically recycle these solutions, bringing them back to the optimization. Three methods of recycling are presented. In order to verify if these ideas are capable to improve the archive content during the optimization, they were implemented together with other techniques from the literature. An computational experiment with NSGA-II, SPEA2, PAES, MOEA/D and NSGA-III algorithms, applied to many classes of problems is presented. The potential and the difficulties of the proposed techniques are evaluated based on statistical tests.

Investigações sobre técnicas de arquivamento para otimizadores multiobjetivo

Multi-objective problems may have many optimal solutions, which together form the Pareto optimal set. A class of heuristic algorithms for those problems, in this work called optimizers, produces approximations of this optimal set. The approximation set kept by the optmizer may be limited or unlimited. The benefit of using an unlimited archive is to guarantee that all the nondominated solutions generated in the process will be saved. However, due to the large number of solutions that can be generated, to keep an archive and compare frequently new solutions to the stored ones may demand a high computational cost. The alternative is to use a limited archive. The problem that emerges from this situation is the need of discarding nondominated solutions when the archive is full. Some techniques were proposed to handle this problem, but investigations show that none of them can surely prevent the deterioration of the archives. This work investigates a technique to be used together with the previously proposed ideas in the literature to deal with limited archives. The technique consists on keeping discarded solutions in a secondary archive, and periodically recycle these solutions, bringing them back to the optimization. Three methods of recycling are presented. In order to verify if these ideas are capable to improve the archive content during the optimization, they were implemented together with other techniques from the literature. An computational experiment with NSGA-II, SPEA2, PAES, MOEA/D and NSGA-III algorithms, applied to many classes of problems is presented. The potential and the difficulties of the proposed techniques are evaluated based on statistical tests.

Análise e desenvolvimento de controladores preditivos multivariáveis baseados em multi-modelos bilineares

This work addresses issues related to analysis and development of multivariable predictive controllers based on bilinear multi-models. Linear Generalized Predictive Control (GPC) monovariable and multivariable is shown, and highlighted its properties, key features and applications in industry. Bilinear GPC, the basis for the development of this thesis, is presented by the time-step quasilinearization approach. Some results are presented using this controller in order to show its best performance when compared to linear GPC, since the bilinear models represent better the dynamics of certain processes. Time-step quasilinearization, due to the fact that it is an approximation, causes a prediction error, which limits the performance of this controller when prediction horizon increases. Due to its prediction error, Bilinear GPC with iterative compensation is shown in order to minimize this error, seeking a better performance than the classic Bilinear GPC. Results of iterative compensation algorithm are shown. The use of multi-model is discussed in this thesis, in order to correct the deficiency of controllers based on single model, when they are applied in cases with large operation ranges. Methods of measuring the distance between models, also called metrics, are the main contribution of this thesis. Several application results in simulated distillation columns, which are close enough to actual behaviour of them, are made, and the results have shown satisfactory

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative

Controlador preditivo generalizado multi-modelo aplicado ao controle de pressão arterial

Postsurgical complication of hypertension may occur in cardiac patients. To decrease the chances of complication it is necessary to reduce elevated blood pressure as soon as possible. Continuous infusion of vasodilator drugs, such as sodium nitroprusside (Nipride), would quickly lower the blood pressure in most patients. However, each patient has a different sensitivity to infusion of Nipride. The parameters and the time delays of the system are initially unknown. Moreover, the parameters of the transfer function associated with a particular patient are time varying. the objective of the study is to develop a procedure for blood pressure control i the presence of uncertainty of parameters and considerable time delays. So, a methodology was developed multi-model, and for each such model a Preditive Controller can be a priori designed. An adaptive mechanism is then needed for deciding which controller should be dominant for a given plant