Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

Proposta de integração das redes de comunicação da COSERN como uma solução de contigêncvia em caso de falhas nos enlaces do backbone de automação

T'his dissertation proposes alternative models to allow the interconnectioin of the data communication networks of COSERN Companhia Energética do Rio Grande do Norte. These networks comprise the oorporative data network, based on TCP/IP architecture, and the automation system linking remote electric energy distribution substations to the main Operatin Centre, based on digital radio links and using the IEC 60870-5-101 protoco1s. The envisaged interconnection aims to provide automation data originated from substations with a contingent route to the Operation Center, in moments of failure or maintenance of the digital radio links. Among the presented models, the one chosen for development consists of a computational prototype based on a standard personal computer, working under LINUX operational system and running na application, developesd in C language, wich functions as a Gateway between the protocols of the TCP/IP stack and the IEC 60870-5-101 suite. So, it is described this model analysis, implementation and tests of functionality and performance. During the test phase it was basically verified the delay introduced by the TCP/IP network when transporting automation data, in order to guarantee that it was cionsistent with the time periods present on the automation network. Besides , additional modules are suggested to the prototype, in order to handle other issues such as security and prioriz\ation of the automation system data, whenever they are travesing the TCP/IP network. Finally, a study hás been done aiming to integrate, in more complete way, the two considered networks. It uses IP platform as a solution of convergence to the communication subsystem of na unified network, as the most recente market tendencies for supervisory and other automation systems indicate

Sistema híbrido para detecção e diagnóstico de falhas em sistemas dinâmicos

The industries are getting more and more rigorous, when security is in question, no matter is to avoid financial damages due to accidents and low productivity, or when it s related to the environment protection. It was thinking about great world accidents around the world involving aircrafts and industrial process (nuclear, petrochemical and so on) that we decided to invest in systems that could detect fault and diagnosis (FDD) them. The FDD systems can avoid eventual fault helping man on the maintenance and exchange of defective equipments. Nowadays, the issues that involve detection, isolation, diagnose and the controlling of tolerance fault are gathering strength in the academic and industrial environment. It is based on this fact, in this work, we discuss the importance of techniques that can assist in the development of systems for Fault Detection and Diagnosis (FDD) and propose a hybrid method for FDD in dynamic systems. We present a brief history to contextualize the techniques used in working environments. The detection of fault in the proposed system is based on state observers in conjunction with other statistical techniques. The principal idea is to use the observer himself, in addition to serving as an analytical redundancy, in allowing the creation of a residue. This residue is used in FDD. A signature database assists in the identification of system faults, which based on the signatures derived from trend analysis of the residue signal and its difference, performs the classification of the faults based purely on a decision tree. This FDD system is tested and validated in two plants: a simulated plant with coupled tanks and didactic plant with industrial instrumentation. All collected results of those tests will be discussed

Eosi: um modelo para desenvolvimento de sistemas embarcados tolerantes a falhas

The semiconductor technologies evolutions leads devices to be developed with higher processing capability. Thus, those components have been used widely in more fields. Many industrial environment such as: oils, mines, automotives and hospitals are frequently using those devices on theirs process. Those industries activities are direct related to environment and health safe. So, it is quite important that those systems have extra safe features yield more reliability, safe and availability. The reference model eOSI that will be presented by this work is aimed to allow the development of systems under a new view perspective which can improve and make simpler the choice of strategies for fault tolerant. As a way to validate the model na architecture FPGA-based was developed.

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

Localização de faltas em linhas de transmissão de múltiplos terminais a partir de registros oscilográficos sincronizados via transformada wavelet

The electric energy is essential to the development of modern society and its increasing demand in recent years, effect from population and economic growth, becomes the companies more interested in the quality and continuity of supply, factors regulated by ANEEL (Agência Nacional de Energia Elétrica). These factors must be attended when a permanent fault occurs in the system, where the defect location that caused the power interruption should be identified quickly, which is not a simple assignment because the current systems complexity. An example of this occurs in multiple terminals transmission lines, which interconnect existing circuits to feed the demand. These transmission lines have been adopted as a feasible solution to suply loads of magnitudes that do not justify economically the construction of new substations. This paper presents a fault location algorithm for multiple terminals transmission lines - two and three terminals. The location method is based on the use of voltage and current fundamental phasors, as well as the representation of the line through its series impedance. The wavelet transform is an effective mathematical tool in signals analysis with discontinuities and, therefore, is used to synchronize voltage and current data. The Fourier transform is another tool used in this work for extract voltage and current fundamental phasors. Tests to validate the location algorithm applicability used data from faulty signals simulated in ATP (Alternative Transients Program) as well as real data obtained from oscillographic recorders installed on CHESF s lines.

Localização de robôs móveis autônomos utilizando fusão sensorial de odometria e visão monocular

The development and refinement of techniques that make simultaneous localization and mapping (SLAM) for an autonomous mobile robot and the building of local 3-D maps from a sequence of images, is widely studied in scientific circles. This work presents a monocular visual SLAM technique based on extended Kalman filter, which uses features found in a sequence of images using the SURF descriptor (Speeded Up Robust Features) and determines which features can be used as marks by a technique based on delayed initialization from 3-D straight lines. For this, only the coordinates of the features found in the image and the intrinsic and extrinsic camera parameters are avaliable. Its possible to determine the position of the marks only on the availability of information of depth. Tests have shown that during the route, the mobile robot detects the presence of characteristics in the images and through a proposed technique for delayed initialization of marks, adds new marks to the state vector of the extended Kalman filter (EKF), after estimating the depth of features. With the estimated position of the marks, it was possible to estimate the updated position of the robot at each step, obtaining good results that demonstrate the effectiveness of monocular visual SLAM system proposed in this paper

Reconhecimento de padrões de falhas em motores trifásicos utilizando redes neurais

This work presents a diagnosis faults system (rotor, stator, and contamination) of three-phase induction motor through equivalent circuit parameters and using techniques patterns recognition. The technology fault diagnostics in engines are evolving and becoming increasingly important in the field of electrical machinery. The neural networks have the ability to classify non-linear relationships between signals through the patterns identification of signals related. It is carried out induction motor´s simulations through the program Matlab R & Simulink R , and produced some faults from modifications in the equivalent circuit parameters. A system is implemented with multiples classifying neural network two neural networks to receive these results and, after well-trained, to accomplish the identification of fault´s pattern

Um método para localização facial Invariante a rotação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Utilização de redes neurais artificiais para detecção e diagnóstico de falhas

In a real process, all used resources, whether physical or developed in software, are subject to interruptions or operational commitments. However, in situations in which operate critical systems, any kind of problem may bring big consequences. Knowing this, this paper aims to develop a system capable to detect the presence and indicate the types of failures that may occur in a process. For implementing and testing the proposed methodology, a coupled tank system was used as a study model case. The system should be developed to generate a set of signals that notify the process operator and that may be post-processed, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and amplifiers of the real plant, the data set of the faults will be computationally generated and the results collected from numerical simulations of the process model. The system will be composed by structures with Artificial Neural Networks, trained in offline mode using Matlab®

Técnicas visuais de localização e mapeamento simultâneos sem extração de primitivas geométricas da imagem

In Simultaneous Localization and Mapping (SLAM - Simultaneous Localization and Mapping), a robot placed in an unknown location in any environment must be able to create a perspective of this environment (a map) and is situated in the same simultaneously, using only information captured by the robot s sensors and control signals known. Recently, driven by the advance of computing power, work in this area have proposed to use video camera as a sensor and it came so Visual SLAM. This has several approaches and the vast majority of them work basically extracting features of the environment, calculating the necessary correspondence and through these estimate the required parameters. This work presented a monocular visual SLAM system that uses direct image registration to calculate the image reprojection error and optimization methods that minimize this error and thus obtain the parameters for the robot pose and map of the environment directly from the pixels of the images. Thus the steps of extracting and matching features are not needed, enabling our system works well in environments where traditional approaches have difficulty. Moreover, when addressing the problem of SLAM as proposed in this work we avoid a very common problem in traditional approaches, known as error propagation. Worrying about the high computational cost of this approach have been tested several types of optimization methods in order to find a good balance between good estimates and processing time. The results presented in this work show the success of this system in different environments

Sistema para detecção e diagnóstico de falhas em motores elétricos de indução utilizando lógica fuzzy

Induction motors are one of the most important equipment of modern industry. However, in many situations, are subject to inadequate conditions as high temperatures and pressures, load variations and constant vibrations, for example. Such conditions, leaving them more susceptible to failures, either external or internal in nature, unwanted in the industrial process. In this context, predictive maintenance plays an important role, where the detection and diagnosis of faults in a timely manner enables the increase of time of the engine and the possibiity of reducing costs, caused mainly by stopping the production and corrective maintenance the motor itself. In this juncture, this work proposes the design of a system that is able to detect and diagnose faults in induction motors, from the collection of electrical line voltage and current, and also the measurement of engine speed. This information will use as input to a fuzzy inference system based on rules that find and classify a failure from the variation of thess quantities

Localização de um robô móvel usando odometria e marcos naturais

Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark

Sistema especialista para detecção de falhas em comandos elétricos

This work consists of the creation of a Specialist System which utilizes production rules to detect inadequacies in the command circuits of an operation system and commands of electric engines known as Direct Start. Jointly, three other modules are developed: one for the simulation of the commands diagram, one for the simulation of faults and another one for the correction of defects in the diagram, with the objective of making it possible to train the professionals aiming a better qualification for the operation and maintenance. The development is carried through in such a way that the structure of the task allows the extending of the system and a succeeding promotion of other bigger and more complex typical systems. The computational environment LabView is employed to enable the system

Avaliação da prevenção de falhas em processos utilizando métodos de tomada de decisão

Este trabalho apresenta uma investigação sobre o emprego de FMEA (Failure Mode and Effect Analysis) de Processo com a exposição de irregularidades na sua utilização. O método AHP (Analytic Hierarchy Process) e os Conjuntos Fuzzy são aplicados no estudo das práticas atuais de utilização de FMEA. O AHP é aplicado para a priorização das irregularidades quanto à gravidade de sua ocorrência. Os Conjuntos Fuzzy são aplicados para avaliação do desempenho da utilização de FMEA em algumas empresas do ramo automotivo. Como resultado, tem-se a aceitação de oito e a não aceitação de três dos onze formulários de FMEA averiguados.