Robust fault detection and estimation for turbofan engines subject to adaptive controllers via observer and ToMFIR techniques

This paper provides a new design of robust fault detection and estimation for turbofan engines with adaptive controllers, the Fully-automatic Digital Engine Controllers (FaDEC). The critical issue is that the FaDEC can depress the faulty effects such that the actual system outputs maintain the pre-specified performance. In addition, observer-based fault-detection strategies are not always reliable because the observer gain may be selected large due to the consideration of stability, robustness or performance, making the magnitude of the fault-detection alarm small. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique is suggested to detect faults in turbofan engines with adaptive controllers. With the aid of system transformation, ToMFIRs and reduced-order Luenberger and learning observers are designed such that reliable fault detection and estimation can be accomplished together. The convergence and stability of the observers are proved. An uncertain model of a turbofan engine is used to verify the proposed ToMFIR and observer techniques for effective and reliable fault detection and estimation.