Localization Method of Loose Particles Based on Chaos Theory and Particle Swarm Optimization-Back-Propagation Neural Network

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.