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.

Assignment and frequency measurement of rotational transitions of (CD3OH)-C-13 by intracavity laser Stark spectroscopy

We present the results of an intracavity Stark spectroscopy experiment on the fundamental state of (CD3OH)-C-13. We use an optically pumped hybrid waveguide FIR laser, CH2F2 as active molecule, and (CD3OH)-C-13 as absorbent molecule. No Brewster window is needed to separate the lasing gas from the absorbing deuterated methanol. An absorption line is assigned as E(l) symmetry (n, K, J): (1,4,18) --> (1,5,18) and its frequency is measured as 63.08631 cm(-1) with a precision of a few parts in 10(7); two more absorptions are reported and a tentative assignment for one of them.

An analog implementation of radial basis neural networks (RBNN) using BiCMOS technology

This paper describes a analog implementation of radial basis neural networks (RBNN) in BiCMOS technology. The RBNN uses a gaussian function obtained through the characteristic of the bipolar differential pair. The gaussian parameters (gain, center and width) is changed with programmable current source. Results obtained with PSPICE software is showed.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Measurement of the rotational inertia of bodies by using mechanical spectroscopy

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

Determination of soil properties from standard penetration test complemented by torque measurement (SPT-T)

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