Monitoring of S&Cs is addressed by focusing on the main components that severely affect the system reliability. The combination of robust diagnostic and prognostic techniques will be an asset to achieve effective predictive maintenance, as shown in other engineering areas. Identification and statistical characterization of the nonlinear phenomena driving the wearing of components will be a primary goal. Methods from signal-based fault diagnosis and change detection will be employed and further developed to meet the high complexity of S&Cs. Estimation methods will be developed to monitor parameters that can be used to make prognosis of wear or faults under development. The research will focus on finding methods that are robust to normal variations due to changing weather conditions or track usage, yet sensitive to changes that indicate component wear. The following results are expected:
- Characterization of the region of fully functional behaviour of S&Cs.
- Identification of S&Cs health indicators to be used for the monitoring of the system.
- Development of a robust condition monitoring system for diagnosis and prognosis of abrupt and incipient deviations from the fully functional behaviour of S&Cs components.