Germano Lambert-Torres

Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Early detection of faults in electrical machines, particularly in induction motors, has become necessary and critical in reducing costs by avoiding unexpected and unnecessary maintenance and outages in industrial applications. Additionally, most of these faults are due to problems in bearings. Thus, in this paper, experimental bearing fault detection of a three-phase induction motor is performed by analyzing the squared envelope spectrum of the stator current. Spectral kurtosis-based algorithms, namely, the fast kurtogram and the wavelet kurtogram, are also applied to improve the envelope analysis. Experimental tests are performed, considering outer bearing faults at different stages, and the results are promising.

Particle swarm optimization for fuzzy membership functions optimization

The use of fuzzy logic to solve control problems have been increasing considerably in the past years. The successfulness of fuzzy application depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. One way to improve the performance of the fuzzy reasoning model is the use of genetic algorithm. In this paper it is shown that a particle swarm optimization (PSO) algorithm learning mechanism, supplements the performance of fuzzy reasoning model. The PSO is able to generate an optimal set of parameters for fuzzy reasoning model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a strategy for the membership functions automatic adjustment, using PSO algorithms, and presents an application designed to park a vehicle into a garage, beginning from any start position.

Hybrid Evolutionary Algorithm Based on PSO and GA Mutation

This paper presents a hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The main idea is to integrate PSO with GA mutation method. Simulations for a series of benchmark test functions show that the hybrid proposed method possess better ability to find the global optimum than the standard PSO algorithm.

Rough Set Theory Fundamental Concepts, Principals, Data Extraction, and Applications

Rough Set Theory, proposed in 1982 by Zdzislaw Pawlak, is in a state of constant development. Its methodology is concerned with the classification and analysis of imprecise, uncertain or incomplete information and knowledge, and of is considered one of the first non-statistical approaches in data analysis (Pawlak, 1982). The fundamental concept behind Rough Set Theory is the approximation of lower and upper spaces of a set, the approximation of spaces being the formal classification of knowledge regarding the interest domain. The subset generated by lower approximations is characterized by objects that will definitely form part of an interest subset, whereas the upper approximation is characterized by objects that will possibly form part of an interest subset. Every subset defined through upper and lower approximation is known as Rough Set. Over the years Rough Set Theory has become a valuable tool in the resolution of various problems, such as: representation of uncertain or imprecise knowledge; knowledge analysis; evaluation of quality and availability of information with respect to consistency and presence a not of date patterns; identification and evaluation of date dependency; reasoning based an uncertain and reduct of information data. The extent of rough set applications used today is much wider than in the past, principally in the areas of medicine, analysis of database attributes and process control. The subject of this chapter is to present the Rough Set Theory, important concepts, and Rough Set Theory used with tools for data mining, special applications in analysis of data in dengue diagnosis. The chapter is divided into the four following topics: • Fundamental concepts • Rough set with tools for data mining • Applications of rough set theory; • Case – Rough set with tools in dengue diagnosis.

Fitting Fuzzy Membership Functions using Hybrid Particle Swarm Optimization

The success of fuzzy application to solve the control problems depends on a number of parameters, such as fuzzy membership functions. One way to improve the performance of the fuzzy reasoning model is made by optimizing the membership functions and the use of evolutionary algorithms. In this paper a Hybrid Particle Swarm Optimization (HPSOM) algorithm is used to optimize the fuzzy membership functions. The HPSOM is able to generate an optimal set of parameters for fuzzy reasoning model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a different strategy for the membership functions automatic adjustment, using HPSOM algorithm. The proposed approach has been examined and tested with promising results using an application designed to park a vehicle into a garage, beginning from any start position.

Smart Impedance: A New Way to Look at Hybrid Filters

Smart impedance is a new way to look at hybrid active power filters. As originally proposed in the literature, hybrid active filters were presented to improve the physical limitation of tuned passive filters. A new conditioner named smart impedance is proposed to eliminate the need of passive filter tuning procedure, the whole tuning process is performed electronically. The proposed topology is able to manipulate the displacement power factor and perform multiple frequency harmonic current mitigation at the same time. In weak systems, where the source impedance is not negligible, like in smart grids, smart impedance guaranties the voltage regulation and stability. The smart impedance control strategy uses proportional resonant controller, which allows phase-locked loop-less power system synchronization. Smart impedance system is composed of an active converter, a coupling transformer, and a capacitive unit, arranged as three phase or single phase equipment.

Induction Motor Efficiency Evaluation Using a New Concept of Stator Resistance

This paper presents an air-gap torque (AGT)-based method for efficiency estimation of induction motors. A new concept of stator resistance that includes the mechanical losses effect is proposed. This new stator resistance is estimated through a particle swarm optimization approach based on the stator flux equations and minimization of torque error at the rated operation point. Then, the obtained stator resistance is used in the AGT equations to estimate the shaft torque and then the efficiency. Moreover, the rotor speed is estimated using induction motor current signature analysis. Thus, the proposed methodology for induction motor efficiency estimation relies only on line currents, line voltages, and nameplate data, being appropriate for in-service applications. Finally, the simulation and experimental results are presented to validate the proposed method at different load conditions.

Intelligent Alarm Processing

This paper presents the analysis that has been carried out in the alarm system of the DCRanger EMS. The intention of this study is to present the problem of alarm processing in electric energy control centers, its various aspects and operational difficulties due to operator needs. Some tests are produced in order to identify the desirable features an alarm system should possess in order to be of effective help in the operative duty.

Paraconsistent Algorithm Extractor of Contradiction Effects - Paraextrctr ctr

Nowadays networks of analyses based in non-classic logics are used with success in the treatment of uncertainties. The characteristic of accepting the contradiction in his structure is the main cause of the methodologies based in Paraconsistent Logic is ideals for applications in systems of analyses and decision making. In this work we presented an algorithm based in Paraconsistent logic capable to extract in a gradual way the effects of the contradiction in originated signals of information of uncertain knowledge database. The Algorithm Paraconsistent Extractor of Contradiction effects - Paraextrctrctr is formed with base in fundamental concepts of the Paraconsistent Annotated Logic with annotation of two values (PAL2v) it can be applied in filters of networks of analyses of signal information where uncertain and contradictory signals can be present. The process of extraction of the effect of the contradiction is always begun by the largest inconsistency degree among two signals that belong to the group that is in analysis. In the end of the analysis it is found a consensus value. In this work we presented numeric example and one example of application of the Paraextrctrctr in Load Profile Forecast used in support to decision of the operation in an Electric Power System, but his application potentiality is demonstrated in several fields of the Artificial Intelligence.

Particle Swarm Optimization Applied to Restoration of Electrical Energy Distribution Systems

This paper presents a technical application of the Particule Swarm Optimization - PSO technique to a reconfiguration problem of a electrical energy distribution system. The proposed methodology consists of the use of the maximization function of the number of loads supplied and the loss minimization by the expansion of the original PSO. The approach utilized the Distribution_System_01. A number of tests were carried out in the system to simulate several fault occurrences in the electrical energy transmission lines. The PSO algorithm encountered the optimal solution in a reasonable CPU time, compared to the dimension of the distribution system.