齿轮箱复合故障信号的非线性盲源分离算法研究

针对齿轮箱复合故障分析问题，文中提出一种新型非线性盲源分离（Nonlinear Blind Source Separation, NBSS）算法。该算法先利用反向传播（Back Propagation, BP）神经网络逼近非线性混合模型的逆，并对经过BP 神经网络处理后的信号进行独立成分分析（Independent Component Analysis, ICA）；然后以独立成分分析后的信号的负熵作为适应度函数，采用遗传算法对BP神经网络的参数进行寻优；最后利用优化的BP神经网络参数，对观测到的混合信号进行分解，分离出纯净的振源信号。与采用粒子群优化（Particle Swarm Optimization, PSO）算法的核独立成分分析（Kernel ICA, KICA）相比，该方法提取的分离信号具有更高的精度，为齿轮箱复合故障诊断提供了关键技术与有效方法。

Research on Nonlinear Blind Source Separation Algorithm of Gearbox Composite Fault Signal

A new nonlinear blind source separation (NBSS) algorithm is proposed to solve the problem of gearbox composite fault analysis in this paper. In this algorithm, the back propagation (BP) neural network is used to approximate the inverse of the nonlinear aliasing model, and the independent component analysis (ICA) algorithm is performed on the signal processed by the BP neural network; then, the negative entropy of the signal after ICA is taken as the fitness function and genetic algorithm is used to optimize the parameters of the BP neural network; finally, using the optimized BP neural network parameters, the observed aliasing signal is decomposed and the pure vibration source signals are separated. Compared with the kernel independent component analysis (KICA) algorithm optimized by the particle swarm optimization (PSO) algorithm, the separated signals extracted by this method have higher accuracy, which provides the key technology and an effective method for the diagnosis of gearbox composite fault.