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Aircraft sensor failure diagnosis using self-organizing fuzzy systems

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 SAFISs are applied as the online approximators for recognizing the sensor outputs in order to determine the failure detection, identification and accommodation (FDIA). SAFIS is an online learning fuzzy system with concurrent structure and parameter learning. The rules of the SAFIS are added or deleted based on the input data without predefining them by trial and error. The efficiency of the proposed scheme is demonstrated by simulation examples where soft failures in the angular rate gyros are successfully diagnosed.

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Aircraft sensor failure diagnosis using self-organizing fuzzy systems

Semantic Scholar · Computer Science · 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 SAFISs are applied as the online approximators for recognizing the sensor outputs in order to determine the failure detection, identification and accommodation (FDIA). SAFIS is an online learning fuzzy system with concurrent structure and parameter learning. The rules of the SAFIS are added or deleted based on the input data without predefining them by trial and error. The efficiency of the proposed scheme is demonstrated by simulation examples where soft failures in the angular rate gyros are successfully diagnosed.

Source of record · Semantic Scholar

References (14)

01Evolving Fuzzy Rules for Anomaly Detection in Data StreamsMasud Moshtaghi, J. Bezdek, C. Leckie et al.2015 · IEEE transactions on fuzzy systems · 53 citations
02Sensor Failure Detection, Identification, and Accommodation Using Fully Connected Cascade Neural NetworkSaed Hussain, M. Mokhtar, J. Howe2015 · IEEE transactions on industrial electronics (1982. Print) · 94 citations
03A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification ProblemsK. Subramanian, S. Sundaram, N. Sundararajan2013 · IEEE transactions on fuzzy systems · 125 citations
04Evolving Fuzzy Systems from Data Streams in Real-TimeP. Angelov, Xiaowei Zhou2006 · 2006 International Symposium on Evolving Fuzzy Systems · 249 citations
05Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and predictionHai-Jun Rong, N. Sundararajan, G. Huang et al.2006 · Fuzzy Sets Syst. · 364 citations
06An approach to online identification of Takagi-Sugeno fuzzy modelsP. Angelov, Dimitar Filev2004 · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 1.0k citations
07On‐line learning neural networks for sensor validation for the flight control system of a B777 research scale modelG. Campa, M. L. Fravolini, B. Seanor et al.2002 · 45 citations
08Fully Tuned Radial Basis Function Neural Networks for Flight ControlNarasimhan Sundararajan, P. Saratchandraw, Yan Li2001 · The Springer International Series on Asian Studies in Computer and Information Science · 82 citations
09Stable Neuro-Flight-Controller Using Fully Tuned Radial Basis Function Neural NetworksYan Li, N. Sundararajan, P. Saratchandran2001 · 49 citations
10Kalman filters and neural-network schemes for sensor validation in flight control systemsM. Napolitano, D. Windon, J. Casanova et al.1998 · IEEE Transactions on Control Systems Technology · 115 citations
11A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural NetworksYingwei Lu, N. Sundararajan, P. Saratchandran1997 · Neural Computation · 546 citations
12Neural-network-based scheme for sensor failure detection, identification, and accommodationM. Napolitano, Charles Neppach, Van Casdorph et al.1995 · 114 citations

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