NDLI: Self-organizing fuzzy failure diagnosis of aircraft sensors

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Self-organizing fuzzy failure diagnosis of aircraft sensors

Content Provider	Springer Nature Link
Author	Lin, Qinying Wang, Xiaoping Rong, Hai Jun
Copyright Year	2015
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.
Starting Page	243
Ending Page	254
Page Count	12
File Format	PDF
ISSN	18659284
Journal	Memetic Computing
Volume Number	7
Issue Number	4
e-ISSN	18659292
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-08-25
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Self-organizing Sensor failure Flight control system Extended sequential adaptive fuzzy inference system ApplicationMathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Control, Robotics, Mechatronics Bioinformatics Applications of Mathematics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Science

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