Skip to main content

Intelligent fault detection for dynamic systems

PhD project. Supervisor: TX Mei

On line fault detection and condition monitoring for dynamic systems are becoming increasingly important because of the potential benefits to detect component failures at their early stages, to prevent further deterioration in performance as well as to ensure timely repair/replacement of faulty components. In the long term, the availability of reliable condition monitoring systems can replace scheduled regular services with maintenance on demand - leading to substantial savings in the total life cycle costs.

This project carries out a fundamental study into a completely new approach, originally proposed by the principle investigator, which offers a simple but very effective means for condition monitoring. The principle is highly innovative which focuses on the comparison of dynamic behaviours between different parts of a system where a symmetry exists in structure and/or in the components used (under the normal condition) and explores changes in dynamic interactions caused by the occurrence of an abnormal condition or component failure. Using a railway vehicle as a case study, the study shows that the proposed condition monitoring technique provides a much improved sensitivity to fault(s), compared to more conventional methods, and is much more robust against uncertainties of external condition changes. The principle is now being studied for condition monitoring for other applications.

The project is financially supported by the Centenary Award (for excellent overseas student).