Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Metodologias para reconhecimento de padrões em sistemas SHM utilizando a técnica da Impedância Eletromecânica (E/M)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise espacial e temporal de atributos do solo e da produtividade das culturas visando manejo específico em áreas agrícolas

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

QK-Means: A clustering technique based on community detection and K-Means for deployment of cluster head nodes

Wireless Sensor Networks (WSN) are a special kind of ad-hoc networks that is usually deployed in a monitoring field in order to detect some physical phenomenon. Due to the low dependability of individual nodes, small radio coverage and large areas to be monitored, the organization of nodes in small clusters is generally used. Moreover, a large number of WSN nodes is usually deployed in the monitoring area to increase WSN dependability. Therefore, the best cluster head positioning is a desirable characteristic in a WSN. In this paper, we propose a hybrid clustering algorithm based on community detection in complex networks and traditional K-means clustering technique: the QK-Means algorithm. Simulation results show that QK-Means detect communities and sub-communities thus lost message rate is decreased and WSN coverage is increased. © 2012 IEEE.

Variabilidade genética de caracteres morfológicos e germinação de Tabebuia caraiba (Mart.) Bur. (Bignoniaceae) no Município de Macapá, AP

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Utilização dos parâmetros físico-químicos e redes neurais artificiais na identificação dos méis de abelhas (Apis mellifera L.) produzidos no verão e inverno na microrregião de Campos de Jordão, São Paulo

The objective of this work was to typify, through physicochemical parameters, honey from Campos do Jordão’s microrregion, and verify how samples are grouped in accordance with the climatic production seasonality (summer and winter). It were assessed 30 samples of honey from beekeepers located in the cities of Monteiro Lobato, Campos do Jordão, Santo Antonio do Pinhal e São Bento do Sapucaí-SP, regarding both periods of honey production (November to February; July to September, during 2007 and 2008; n = 30). Samples were submitted to physicochemical analysis of total acidity, pH, humidity, water activity, density, aminoacids, ashes, color and electrical conductivity, identifying physicochemical standards of honey samples from both periods of production. Next, we carried out a cluster analysis of data using k-means algorithm, which grouped the samples into two classes (summer and winter). Thus, there was a supervised training of an Artificial Neural Network (ANN) using backpropagation algorithm. According to the analysis, the knowledge gained through the ANN classified the samples with 80% accuracy. It was observed that the ANNs have proved an effective tool to group samples of honey of the region of Campos do Jordao according to their physicochemical characteristics, depending on the different production periods.

Transient stability analysis of electric energy systems via a fuzzy ART-ARTMAP neural network

This work presents a methodology to analyze transient stability (first oscillation) of electric energy systems, using a neural network based on ART architecture (adaptive resonance theory), named fuzzy ART-ARTMAP neural network for real time applications. The security margin is used as a stability analysis criterion, considering three-phase short circuit faults with a transmission line outage. The neural network operation consists of two fundamental phases: the training and the analysis. The training phase needs a great quantity of processing for the realization, while the analysis phase is effectuated almost without computation effort. This is, therefore the principal purpose to use neural networks for solving complex problems that need fast solutions, as the applications in real time. The ART neural networks have as primordial characteristics the plasticity and the stability, which are essential qualities to the training execution and to an efficient analysis. The fuzzy ART-ARTMAP neural network is proposed seeking a superior performance, in terms of precision and speed, when compared to conventional ARTMAP, and much more when compared to the neural networks that use the training by backpropagation algorithm, which is a benchmark in neural network area. (c) 2005 Elsevier B.V. All rights reserved.

Transient stability analysis of electrical power systems using a neural network based on fuzzy ARTMAP

This work presents a methodology to analyze transient stability for electric energy systems using artificial neural networks based on fuzzy ARTMAP architecture. This architecture seeks exploring similarity with computational concepts on fuzzy set theory and ART (Adaptive Resonance Theory) neural network. The ART architectures show plasticity and stability characteristics, which are essential qualities to provide the training and to execute the analysis. Therefore, it is used a very fast training, when compared to the conventional backpropagation algorithm formulation. Consequently, the analysis becomes more competitive, compared to the principal methods found in the specialized literature. Results considering a system composed of 45 buses, 72 transmission lines and 10 synchronous machines are presented. © 2003 IEEE.

Characterization of the temperature, load and damage effects using piezoelectric transducer patches based on fuzzy clustering

Structural Health Monitoring (SHM) denotes a system with the ability to detect and interpret adverse changes in a structure. One of the critical challenges for practical implementation of SHM system is the ability to detect damage under changing environmental conditions. This paper aims to characterize the temperature, load and damage effects in the sensor measurements obtained with piezoelectric transducer (PZT) patches. Data sets are collected on thin aluminum specimens under different environmental conditions and artificially induced damage states. The fuzzy clustering algorithm is used to organize the sensor measurements into a set of clusters, which can attribute the variation in sensor data due to temperature, load or any induced damage.

Application of an iterative method and an evolutionary algorithm in fuzzy optimization

This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain. © 2012 Brazilian Operations Research Society.

Estimação da densidade de solos utilizando sistemas de inferência fuzzy

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Classificador Fisherface Fuzzy para o reconhecimento de faces

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimating outcomes in newborn infants using fuzzy logic

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)