By Ihab Samy
This booklet considers well known themes: fault detection and isolation (FDI) and flight information estimation utilizing flush air facts sensing (FADS) structures. Literature surveys, comparability checks, simulations and wind tunnel assessments are played. In either circumstances, a UAV platform is taken into account for demonstration reasons. within the first a part of the ebook, FDI is taken into account for sensor faults the place a neural community procedure is applied. FDI is utilized either in academia and leading to many courses over the last 50 years or so. even though few courses contemplate neural networks compared to conventional recommendations akin to observer established, parameter estimations and parity area ways. the second one a part of this booklet makes a speciality of tips to estimate flight information (angle of assault, airspeed) utilizing a matrix of strain sensors and a neural community version. In end this e-book can function an creation to FDI and FADS platforms, a literature survey, and a case examine for UAV functions.
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Additional resources for Fault Detection and Flight Data Measurement: Demonstrated on Unmanned Air Vehicles using Neural Networks
The method used to train the NN has also been discussed in this chapter and it was noted that online training in the FADS system is not practically possible due to the requirement of additional instrumentation such as an air data boom. Finally, the NN training stopping criteria were defined based on the need to check for NN convergence and to avoid over-fitting the NN structure. Chapter 5 SFDA-Single Sensor Faults Introduction In this chapter, a NN-based and EKF-based sensor fault detection and accommodation (SFDA) scheme are proposed and compared under different levels of unknown inputs and fault types.
E. 3). e. target) NN outputs must be defined a priori so that the estimation error e can be calculated at each time step. This method of training is more formally known as supervised training. Supervised training can be either implemented online or offline. g. by judging the accuracy and generalisation capability of the NN) the NN structure is frozen and used on board the aircraft with no further training. On the other hand online training continuously updates the NN structure in real time and is generally preferable in time-varying systems.
Note that in NN-based SFDA schemes, NN training is switched off once a fault is detected to avoid learning faulty measurements. 2 UAV Used in the SFDA Schemes 39 To summarise the notations used in this chapter consider the following statement: “In this chapter four SFDA schemes are implemented, NN-RGE, NN-RGPE, EKFRGE, EKF-RGPE. Faults in Sensor-q are only considered and the residual compares qreal and (or ). ” Fig. 2 UAV Used in the SFDA Schemes The UAV is based on the Eclipse class vehicle  and is powered by a small gas turbine engine.
Fault Detection and Flight Data Measurement: Demonstrated on Unmanned Air Vehicles using Neural Networks by Ihab Samy