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Erschienen in:
01.12.2015 | Regular Research Paper
Self-organizing fuzzy failure diagnosis of aircraft sensors
Erschienen in: Memetic Computing | Ausgabe 4/2015
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Abstract
A novel scheme for diagnosing sensor failures in a flight control system is presented. In the proposed scheme, a set of self-organizing fuzzy systems named as extended sequential adaptive fuzzy inference systems (ESAFISs) are applied as the online approximators for recognizing the sensor outputs. ESAFIS is an online learning fuzzy system with concurrent structure and parameter learning. The rules of the ESAFIS are added or deleted based on the input data without predefining them by trial and error. From an analysis of the residual signals between the estimated states and the measurements of the sensors, the failure diagnosis by determining the failure detection, identification and accommodation can be achieved. The efficiency of the proposed scheme is demonstrated by simulation examples where hard and soft failures in the angular rate gyros are successfully diagnosed.