scholarly journals A Novel Strong Tracking Fault Prognosis Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Qi Zhang ◽  
Wei Jiang ◽  
Tian-Mei Li ◽  
Jian-Fei Zheng
Keyword(s):  
Particle Filter ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Fault Prognosis ◽  
Artificial Immunity ◽  
Simulation Results ◽  
Strong Tracking Filter ◽  
Tracking Filter ◽  
Degeneracy Problem

Improving the ability to track abruptly changing states and resolving the degeneracy are two difficult problems to particle filter applied to fault prognosis. In this paper, a novel strong tracking fault prognosis algorithm is proposed to settle the above problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. The particles are updated by strong tracking filter, and better particles are selected by utilizing the artificial immune algorithm to estimate states. As a result, the degeneracy problem is resolved and the accuracy of the proposed fault prognosis algorithm is improved accordingly. The feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system.

A Fault Predication Algorithm Based on Artificial Immune Particle Filter

2010 ◽  
Vol 44-47 ◽  
pp. 3459-3463 ◽  
Author(s):  
Yu Kun Qiao ◽  
Qi Zhang ◽  
Jin Sheng Zhang
Keyword(s):  
Particle Filter ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Large Weight ◽  
Tracking Ability ◽  
Simulation Results ◽  
Degeneracy Problem ◽  
Sir Particle Filter

Degeneracy problem is an inevitable result of sequential importance re-sampling (SIR) particle filter, and a mass of degenerated particles will influence the tracking ability of particle filter seriously. As a result, SIR particle filter based predication algorithm can’t predict system faults accurately. Artificial immune algorithm is characterized by a global ability to search for optimum, so it is introduced into the particle filter, named artificial immune particle filter (AIPF). Particles are regarded as antibodies in AIPF and particles with large weight aberrance and are cloned, and then the better particles are selected for states evaluation. A fault predication algorithm based on AIPF is proposed to improve the predication accuracy, and simulation results have demonstrated the feasibility of the proposed algorithm.

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