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Information Technology Journal
Year: 2012  |  Volume: 11  |  Issue: 10  |  Page No.: 1496 - 1501

APSO-RVM for Fault Detection of Liquid Rocket Engines Test-bed

Fengyu Zhu, Qi Wang and Zhengguang Shen    

Abstract: 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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