Fusion of Model-Based and Data Driven Based Fault Diagnostic Methods for Railway Vehicle Suspension

Transportation of freight and passengers by train is one of the oldest types of transport, and has now taken root in most of the developing countries especially in Africa. Recently, with the advent and development of high-speed trains, continuous monitoring of the railway vehicle suspension is of significant importance. For this reason, railway vehicles should be monitored continuously to avoid catastrophic events, ensure comfort, safety, and also improved performance while reducing life cycle costs. The suspension system is a very important part of the railway vehicle which supports the car-body and the bogie, isolates the forces generated by the track unevenness at the wheels and also controls the attitude of the car-body with respect to the track surface for ride comfort. Its reliability is directly related to the vehicle safety. The railway vehicle suspension often develops faults;worn springs and dampers in the primary and secondary suspension. To avoid a complete system failure, early detection of fault in the suspension of trains is of high importance. The main contribution of the research work is the prediction of faulty regimes of a railway vehicle suspension based on a hybrid model. The hybrid model framework is in four folds;first, modeling of vehicle suspension system to generate vertical acceleration of the railway vehicle, parameter estimation or identification was performed to obtain the nominal parameter values of the vehicle suspension system based on the measured data in the second fold, furthermore, a supervised machine learning model was built to predict faulty and healthy state of the suspension system components (damage scenarios) based on support vector machine (SVM) and lastly, the development of a new SVM model with the damage scenarios to predict faults on the test data. The level of degradation at which the spring and damper becomes faulty for both primary and secondary suspension system was determined. The spring and damper becomes faulty when the nominal values degrade by 50% and 40% and 30% and 40% for the secondary and primary suspension system respectively. The proposed model was able to predict faulty components with an accuracy of 0.844 for the primary and secondary suspension system.

Observer-Based Disturbance Accommodation Control Strategy for Useful Lifetime Control and Structural Load Mitigation of Wind Turbines

Wind turbines undergo degradation due to various factors which induce stress, thereby leading to fatigue damage to various wind turbine components. In addition, the current increase in demand for electrical power has led to the development of large wind turbines, which result in increased structural loads, therefore, increasing the possibility of early failure due to fatigue load. This paper proposes a proportional integral observer (PI-Observer) based disturbance accommodation controller (DAC) with individual pitch control (IPC) for load mitigation to reduce components’ damage and ensure the wind turbine is operational for the expected lifetime. The results indicate a reduction in blades’ bending moments with a standard deviation of 15.9%, which positively impacts several other wind turbine subsystems. Therefore, the lifetime control strategy demonstrates effective structural load mitigation without compromise on power generation, thus, achieving a nominal lifetime control to inhibit premature failure.