This paper presents a fault detection and isolation (FDI) approach for actuator faults of complex thermal management systems. In the case of safety critical systems, early fault diagnosis not only improves system reliability, but can also help prevent complete system failure (i.e., aircraft system). In this work, a robust unknown input observer (UIO)-based actuator FDI approach is applied on an example aircraft fluid thermal management system (FTMS). Robustness is achieved by decoupling the effect of unknown inputs modeled as additive disturbances (i.e., modeling errors, linearization errors, parameter variations, or model order reduction errors) from the residuals generated from a bank of UIOs. Robustness is central to avoid false alarms without reducing residual sensitivity to actual faults in the system. System dynamics are modeled using a graph-based approach. A structure preserving aggregation-based model-order reduction technique is used to reduce the complexity of the dynamic model. A reduced-order linearized state space model is then used in a bank of UIOs to generate a set of structured robust (in the sense of disturbance decoupling) residuals. Simulation and experimental results show successful (i.e., no false alarms) actuator FDI in the presence of unknown inputs.
Model Order Reduction of Linear Time Interval System Using Stability Equation Method and a Soft Computing Technique
