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This paper presents a method of detecting aircraft icing by monitoring its effects on aircraft dynamics. This paper shows that uncontrolled icing on control surfaces directly influences control effectiveness. Using data from onboard attitude and navigation sensors via highly computationally efficient algorithms, the control effectiveness is estimated, thereby detecting icing. Using actual flight test data from NASA Lewis Research Center, this paper demonstrates the ability of this method to detect the loss of elevator effectiveness that occurs with uncontrolled horizontal stabilizer icing that could result from a failed deicing boot. The method is generally applicable to loss of control effectiveness due to icing. Icing affects the aerodynamic performance of aircraft by contaminating the aerodynamic surfaces. Without anti-icing equipment icing, if sufficiently severe, can relatively quickly lead to a situation in which controllable flight is impossible.

This paper presents the application of detection filters to the diagnosis of sensor and actuator failures in automotive control systems. The detection filter is the embodiment of a model-based failure detection and isolation (FDI) methodology, which utilizes analytical redundancy within a dynamical system (e.g., engine/controller) to isolate the cause and location of abnormal behavior (i.e., failures). The FDI methodology has been used, among other applications, in the aerospace industry for fault diagnosis of inertial navigation systems and flight controllers. This paper presents the philosophy and essential features of FDI theory, and describes the practical application of the method to the diagnosis of faults in the throttle position sensor in an electronically controlled IC engine. The paper also discusses the incorporation of FDI systems in the design process of a control strategy, with the aim of increasing reliability by embedding diagnostic features within the control strategy.