【摘要】 为解决BP神经网络收敛速度慢以及容易陷入局部最优解的问题,将遗传算法与BP神经网络相结合应用于轴系故障诊断中。首先设计了船舶柴油机轴系模拟实验平台,然后利用小波包分解技术分析了轴系故障时的振动信号,并将其能谱熵作为故障模式的特征向量,最后对轴系的4种运行状态进行了故障识别与分析。仿真结果表明,GA-BP算法预测精度要高于传统的BP算法,适用于轴系的状态监测和故障诊断。更多还原

【Abstract】 In order to solve the problems of slow convergence rate and falling into local minimum easily of BP neural network,a diagnosis method to combine the neural network with the genetic algorithm was investigated.Firstly,a simulation platform for marine diesel engine shafting was designed.Then,by analyzing the torsional vibration signal in the decomposition of wavelet packet when marine diesel engine’s rotating shaft system failed,the energy spectrum entropy of wavelet packet was extracted as the feature vector of failure patterns.Finally,four kinds of operation condition were identified by genetic neural network.The experimental results show that GA-BP can get higher forecast accuracy than the conventional BP in the task of simulation,which is suitable to the condition monitoring and fault diagnosis of rotating shaft system.更多还原