遗传优化LVQ神经网络在设备故障诊断系统中的应用

Application of genetic optimization lvq neural network in equipment fault diagnosis system

为了提高设备故障诊断的准确度,采用LVQ神经网络来完成设备故障定位及识别,并借助遗传算法求解LVQ神经网络权重初始值。在设备故障诊断的建模过程中,根据实际故障情况和故障常见类别建立LVQ神经网络的设备故障诊断模型,充分挖掘LVQ神经网络在机械设备故障诊断细粒度的优势,为了防止因为故障细粒度诊断而造成收敛过慢的情况,对LVQ神经网络的权重和阈值初值进行遗传算法求解,然后在进行LVQ神经网络的迭代训练,得到稳定的LVQ神经网络故障诊断模型。经过实验证明,相比于传统的LVQ神经网络算法,采用基于遗传算法优化LVQ神经网络的设备故障分类,分类准确度更高,训练时间更快。

In order to improve the accuracy of equipment fault diagnosis,LVQ neural network is used to locate and identify equipment faults,and genetic algorithm is used to solve the initial weight value of LVQ neural net work.In the modeling process of equipment fault diagnosis,an equipment fault diagnosis model of LVQ neural network is established according to actual fault conditions and common types of faults,and the advantage of LVQ neural network in fine-grained fault diagosis of mechanical equipment is fully exploited.In order to prevent the slow convergence caused by fine-grained fault diagnosis,the weight and threshold initial value of LVQ neural net work are solved by genetic algorithm,and then iterative training of LVQ neural network is carried out to obtain a stable LVQ neural network fault diagnosis model.Experiments show that the equipment fault classification which based on genetic algorithm optimization LVQ neural network has higher classification accuracy and faster training time,compared with the traditional LVQ neural network algorithm.