Fault tolerant strategy for actively controlled railway wheelset

Traditionally, solid axle railway wheelsets are stabilised by using passive suspensions on a conventional rail vehicle, but such additional stiffness affects the pure rolling action of the wheelset around the curve. It has been theoretically proven that this design conflict between stability and curving performance can be solved by applying active control instead of conventional passive components, resulting in the reduction of the wear of the wheels and track by minimising the track shifting forces. In the active approach, the use of actuators, sensors and data processors to replace the traditional passive suspension raises the issue of the system safety in the event of a failure of the active control, which could result in the loss of stability and in more severe cases, derailment. Further on, in active control systems for railway vehicles the actuators tend to be significantly more expensive and require more additional space than sensors, and an electronic control unit. Therefore, developing an analytical redundancy-based fault tolerance technique for an actively controlled wheelset that minimises the number of actuators will clearly be more beneficial. Thus the emphasis of this research is to develop a fault-tolerant system of active control for a railway vehicle in the event of actuator malfunction in order to guarantee stability and good curving performance without using additional actuators. The key achievements of this research can be summarised as follows: •The research considers three of the most common types of actuator failure for the electro-mechanical actuators: fail-hard (FH), short circuit (SC) and open circuit (OC). The fail-hard is a failure condition when the motor shaft of the actuator becomes immovable, whereas the short circuit and open circuit are failures that occur in the electrical parts of the actuator which correspond to zero voltage and zero current in the motor respectively.•The research investigates and develops a thorough understanding of the effect of actuator faults and failure modes on the vehicle behaviour that provides the necessary foundation for the development of the proposed fault-tolerant strategy.•An effective fault detection and isolation methods for actuator faults through two different approaches is developed; the vehicle model-based approach and the actuator model-based approach. Additionally, the research takes into account the reliability and robustness of the FDI schemes in the presence of sensor failures and parameter uncertainties in the system. •The research develops the control re-configuration in order to cope with the identified failure mode of the actuator in order to maintain the vehicle stability and desired curving performance

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements