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.

Desenvolvimento e implementação de um sistema para detecção de falhas em estruturas usando microcontrolador

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoramento da Integridade Estrutural de Sistemas Mecânicos via Observador de Estado Modal

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Monitoramento da integridade em estruturas aeronáuticas

Pós-graduação em Engenharia Mecânica - FEIS

Uma Contribuição aos Sistemas de Monitoramento de Integridade Estrutural Baseados na Impedância Eletromecânica

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Monitoramento de integridade estrutural baseada em sensores piezelétricos e análise de sinais no domínio do tempo

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

Efeitos de descontinuidades na propagação de ondas em estruturas unidimensionais

Pós-graduação em Engenharia Mecânica - FEIS

Modeling of piezoelectric energy harvesting considering the dependence of the rectifier circuit

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo de sensores PZT aplicados à detecção de danos em tubulações

Pós-graduação em Engenharia Elétrica - FEIS

Monitoramento e identificação de falhas em estruturas aeronáuticas e mecânicas utilizando técnicas de computação inteligente

Pós-graduação em Engenharia Mecânica - FEIS

Identificação de forças de excitação em sistemas rotativos utilizando funções ortogonais

Pós-graduação em Engenharia Mecânica - FEIS

Detecção de danos em estruturas guiadas usando ondas de alta frequência

Pós-graduação em Engenharia Mecânica - FEIS

Effects of railway track vibration induced by passing trains on an energy harvesting device

Pós-graduação em Engenharia Mecânica - FEIS

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

Structural FRF acquisition via electric impedance measurement applied to damage location

Continuing development of new materials makes systems lighter and stronger permitting more complex systems to provide more functionality and flexibility that demands a more effective evaluation of their structural health. Smart material technology has become an area of increasing interest in this field. The combination of smart materials and artificial neural networks can be used as an excellent tool for pattern recognition, turning their application adequate for monitoring and fault classification of equipment and structures. In order to identify the fault, the neural network must be trained using a set of solutions to its corresponding forward Variational problem. After the training process, the net can successfully solve the inverse variational problem in the context of monitoring and fault detection because of their pattern recognition and interpolation capabilities. The use of structural frequency response function is a fundamental portion of structural dynamic analysis, and it can be extracted from measured electric impedance through the electromechanical interaction of a piezoceramic and a structure. In this paper we use the FRF obtained by a mathematical model (FEM) in order to generate the training data for the neural networks, and the identification of damage can be done by measuring electric impedance, since suitable data normalization correlates FRF and electrical impedance.