Abstract
With various modeling technologies applied, the sensor fault detection and isolation scheme based on the decentralized model (also referred to as dedicated observer scheme) becomes a popular approach for sophisticated systems. However, the commonly used modeling approach in many literatures that directly takes measurement values as model inputs may result in residual crosstalks and even false alarms. In this paper, the traditional decentralized model scheme is analyzed and a novel scheme based on the time window interactive prediction structure is proposed. Then, the Elman neural network is applied to model identification due to its nonlinear approximation and online learning properties. Finally, Simulations for comparison using the decoupled longitudinal motion model of some airplane are performed, and the results show that the proposed scheme has higher detection speed, lower false alarm rate and less undetected faults.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
