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.

Fault-tolerant aspects of MPC

The architecture of model predictive control (MPC), with its explicit internal model and constrained optimization is presented. Since MPC relies on an explicit internal model, one can imagine dealing with failures by updating the internal model, and letting the on-line optimizer work out how to control the system in its new condition. This aspects rely on assumptions such that the nature of the fault can be located, and the model can be updated automatically. A standard form of MPC, with linear inequality constraints on inputs and outputs, linear internal model, and quadriatic cost function.

Fault-tolerant aspects of MPC

The various aspects of fault-tolerant control systems that have the ability to survive major equipment failures or damages are discussed. Model predictive control (MPC) offers a promising basis for fault-tolerant control. Failures can be dealt with by updating internal models and letting the on-line optimizer control the system in its new condition. Fault detection and isolation (FDI) and the management of complex models are two emerging technologies in this field.

Numerical analysis of the current and voltage sharing issues for resistive fault current limiter using YBCO coated conductors

YBaCuO-coated conductors offer great potential in terms of performance and cost-saving for superconducting fault current limiter (SFCL). A resistive SFCL based on coated conductors can be made from several tapes connected in parallel or in series. Ideally, the current and voltage are shared uniformly by the tapes when quench occurs. However, due to the non-uniformity of property of the tapes and the relative positions of the tapes, the currents and the voltages of the tapes are different. In this paper, a numerical model is developed to investigate the current and voltage sharing problem for the resistive SFCL. This model is able to simulate the dynamic response of YBCO tapes in normal and quench conditions. Firstly, four tapes with different Jc 's and n values in E-J power law are connected in parallel to carry the fault current. The model demonstrates how the currents are distributed among the four tapes. These four tapes are then connected in series to withstand the line voltage. In this case, the model investigates the voltage sharing between the tapes. Several factors that would affect the process of quenches are discussed including the field dependency of Jc, the magnetic coupling between the tapes and the relative positions of the tapes. © 2010 IEEE.