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Self-organizing fuzzy failure diagnosis of aircraft sensors

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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.

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Acknowledgments

This work is funded in part by National Science Council of ShaanXi Province (Grant No. 2014JM8337), National Natural Science Foundation of China (Grant No. 61004055) and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics Engineering, Air Force Engineering Univerisity, Xi’an, 710038, Shaanxi, People’s Republic of China

    Qinying Lin & Xiaoping Wang

  2. State Key Laboratory for Strength and Vibration of Mechanical Structures, School of Aerospace, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, People’s Republic of China

    Hai-Jun Rong

Authors
  1. Qinying Lin
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  2. Xiaoping Wang
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  3. Hai-Jun Rong
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Correspondence to Hai-Jun Rong.

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Lin, Q., Wang, X. & Rong, HJ. Self-organizing fuzzy failure diagnosis of aircraft sensors. Memetic Comp. 7, 243–254 (2015). https://doi.org/10.1007/s12293-015-0167-9

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  • DOI: https://doi.org/10.1007/s12293-015-0167-9

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