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Localization Method of Loose Particles Based on Chaos Theory and Particle Swarm Optimization-Back-Propagation Neural Network
- Zhigang Sun - Heilongjiang University, Electronic Engineering College, China Harbin Institute of Technology, Reliability Institute for Electric Apparatus and Electronics, China ,
- Guotao Wang - Heilongjiang University, Electronic Engineering College, China Harbin Institute of Technology, Reliability Institute for Electric Apparatus and Electronics, China ,
- Mengmeng Gao - Heilongjiang University, Electronic Engineering College, China ,
- Yajie Gao - Heilongjiang University, Electronic Engineering College, China ,
- Liang Guo - Harbin Institute of Technology, Reliability Institute for Electric Apparatus and Electronics, China
Journal Article
01-15-02-0012
ISSN: 1946-3855, e-ISSN: 1946-3901
Sector:
Citation:
Sun, Z., Wang, G., Gao, M., Gao, Y. et al., "Localization Method of Loose Particles Based on Chaos Theory and Particle Swarm Optimization-Back-Propagation Neural Network," SAE Int. J. Aerosp. 15(2):185-196, 2022, https://doi.org/10.4271/01-15-02-0012.
Language:
English
Abstract:
Loose particles inside the additional pipe of a rocket engine are an important
factor that causes propulsion system failure. For loose particles inside the
additional pipe, it is necessary not only to determine whether they exist or
not, but also to locate them for subsequent processing. Due to the complex
structure of the additional pipe, the uneven medium used for sound wave
transmission, and the anisotropic speed of the sound. Thus, it is difficult to
determine the locations of loose particles by using the traditional time
difference localization method. Aiming at this problem, this article proposed a
localization method of loose particles based on Chaos Theory and Particle Swarm
Optimization-Back-Propagation Neural Network (PSO BP Neural Network). First,
chaotic characteristics of collision signals generated by loose particles are
studied. On this basis, the localization method of loose particles based on PSO
BP Neural Network is proposed, which uses the correlation dimension, Lyapunov
exponent, and the Kolmogorov entropy (K entropy) as localization features. The
test results show that the proposed loose particle localization method can
effectively locate loose particles inside a section of broken line pipe, which
is composed of composite materials and have a certain internal structure. The
method can theoretically be applied to the localization of collision signals
with similar generation mechanism.