Nanostructuring of materials for device and sensor applications

The recent developments in nanotechnology are reviewed, with particular emphasis on its application in microsystem technology where increased reliability is achieved by integrating the sensor and the readout electronics on the same substrate. New applications may be possible using integrated micromechanical clips to connect optic fibers and components in integrated silicon systems. Some of the key developments in enabling technologies are also described, including the control of thin film deposition, nanostructuring to tailor the properties of thin film, silicon micromachining to make sensors, and microclips for the low-cost assembly of integrated optical microsystems.

Probabilistic fault identification using vibration data and neural networks

Bayesian formulated neural networks are implemented using hybrid Monte Carlo method for probabilistic fault identification in cylindrical shells. Each of the 20 nominally identical cylindrical shells is divided into three substructures. Holes of (12±2) mm in diameter are introduced in each of the substructures and vibration data are measured. Modal properties and the Coordinate Modal Assurance Criterion (COMAC) are utilized to train the two modal-property-neural-networks. These COMAC are calculated by taking the natural-frequency-vector to be an additional mode. Modal energies are calculated by determining the integrals of the real and imaginary components of the frequency response functions over bandwidths of 12% of the natural frequencies. The modal energies and the Coordinate Modal Energy Assurance Criterion (COMEAC) are used to train the two frequency-response-function-neural-networks. The averages of the two sets of trained-networks (COMAC and COMEAC as well as modal properties and modal energies) form two committees of networks. The COMEAC and the COMAC are found to be better identification data than using modal properties and modal energies directly. The committee approach is observed to give lower standard deviations than the individual methods. The main advantage of the Bayesian formulation is that it gives identities of damage and their respective confidence intervals.