基于GA-PSO优化BP神经网络的压缩机气阀故障诊断

针对压缩机气阀故障信号非平稳性、非周期性的特点，提出一种基于主成分分析（PCA）和GA-PSO优化BP神经网络的压缩机气阀故障诊断方法。首先利用小波包分解提取出气阀故障的特征；然后故障特征向量通过PCA降维，降低网络的规模和计算时间。针对标准BP算法收敛速度慢且易陷入局部极小的缺点，引入一种GA-PSO算法用于BP神经网络的参数优化过程。最后以往复压缩机阀盖的振动信号作为信号源，通过故障诊断仿真测试，验证了PCA和GA-PSO-BP神经网络对压缩机气阀故障诊断具有可行性和有效性。

Fault Diagnosis of Compressor Gas Valve Based on BP Neural Network of a Particle Swarm Genetic Algorithm

Aiming at the characteristics of faults signals of the gas valve:strong non-stationary and aperiodic, a fault diagnosis method of reciprocating compressor valve is proposed based on principal component analysis (PCA) and back-propagation (BP) neural network of a genetic algorithm and particle swarm optimization (GA-PSO). First of all, the features of valve faults are extracted by wavelet packet decomposition; then fault feature vectors are dimensionally reduced by using PCA for reducing the scale and computing time of the network. Since the traditional BP algorithm has slow convergence speed and is easy to fall into local minimum, a GA-PSO is employed to optimize the parameters of BP neural network. Finally, using the vibration signal of valve cover of reciprocating compressor as research object, the simulation tests show that PCA and BP neural network of the GA-PSO is feasible and effective for the reciprocating compressor valve fault diagnosis.