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Information Technology Journal
  Year: 2012 | Volume: 11 | Issue: 10 | Page No.: 1496-1501
DOI: 10.3923/itj.2012.1496.1501
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APSO-RVM for Fault Detection of Liquid Rocket Engines Test-bed

Fengyu Zhu, Qi Wang and Zhengguang Shen

Selection of Relevance Vector Machine (RVM) kernel function parameter is one among ineffectively resolved issues which is first resolved in the literature by Adaptive Particle Swarm Optimization (APSO). A novel APSO-RVM method is proposed to optimize and select the RVM kernel parameter, thus forming, taking the advantage of APSO dramatically convergence. Furthermore, the method is applied to the fault detection of liquid rocket engines test-bed. In order to verify the validity of dramatically effectiveness in fault detection, this paper demonstrates the proposed APSO-RVM approach by performing both simulations and experiments using Oxygen Valve Outlet Pressure (Pejy) data. Results show that APSO-RVM can rapidly detect faults effectively and has a high practical value.
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  •    Segmented Tracks Planning of Roadway-Powered System for Electric Vehicles using Improved Particle Swarm Optimization
  •    An Improved PSO Algorithm Coupling with Prior Information for Function Approximation
  •    Missile Fault Detection Based on Linear Parameter Varying Fault Detection Filter
How to cite this article:

Fengyu Zhu, Qi Wang and Zhengguang Shen, 2012. APSO-RVM for Fault Detection of Liquid Rocket Engines Test-bed. Information Technology Journal, 11: 1496-1501.

DOI: 10.3923/itj.2012.1496.1501






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