Fault Diagnosis Model of Suspension System Based on Robust Observer

In the discipline of control engineering, the field of fault diagnosis technology stands as a critical component for ensuring system stability and safety. This research concentrates on fault identification and treatment within the suspension control system of a maglev train, providing a theoretical foundation for fault diagnosis through the establishment of a suspension module model encompassing both disturbance and potential faults. To bolster the robustness of fault diagnosis, a fault diagnosis strategy employing a robust observer is proposed, capable of not only detecting faults, but also isolating faults within actuators and sensors. Furthermore, this paper compares the application of sliding mode observer and adaptive observer in fault diagnosis, with results demonstrating that the adaptive observer exhibits superior performance in fault diagnosis. Building upon this, the paper delves into a discussion of a fault detection method that combines model and data-driven approaches, enhancing fault detection accuracy.