In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine\ngas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal\nparticle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure,\nwhich adjusts the position of the particles through simulating attractionââ?¬â??repulsion mechanism between charged particles\nof the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the\ndiversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement\ninformation. Comparison of the proposed method with three different filter algorithms is carried out on a univariate\nnonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal\nparticle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period\nand the root mean square error of parameters estimation is reduced.
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