Journal of Guidance, Control, and Dynamics
A class of FDI (fault detection and identification) methods for bias-type actuator and sensor faults was explored from the point of view of fault identifiability. The methods use banks of Kalman-Bucy filters (KBFs) to detect faults, determine the fault pattern, and estimate the fault values. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults was presented. It was shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions were demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.
Original Publication Citation
Joshi, S. M., González, O. R., & Upchurch, J. M. (2014). Identifiability of additive actuator and sensor faults by state augmentation. Journal of Guidance, Control, and Dynamics, 37(3), 941-946. doi:10.2514/1.62523
Joshi, Suresh M.; Gonzalez, Oscar R.; and Upchurch, Jason M., "Identifiability of Additive Actuator and Sensor Faults by State Augmentation" (2014). Electrical & Computer Engineering Faculty Publications. 229.