Fault identification using finite element models and neural networks

When vibration data are used to identify faults in structures it is not completely clear whether to use either frequency response functions or modal parameters. This paper presents a committee of neural networks technique, which employs both frequency response functions and modal data simultaneously to identify faults in structures. The new approach is tested on simulated data from a cantilevered beam, which is substructured into five regions. It is observed that irrespective of the noise levels in the data, the committee of neural networks gives results that have lower mean-squares errors and standard deviations than the two existing methods. It is found that the new method is able to identify fault cases better than the two approaches used individually. It is established that for the problem analyzed, giving equal weights to the frequency-response-based method and modal-properties-based method minimize the errors on identifying faults.