基于LM-BP神经网络的气阀故障诊断方法

提出一种基于LM（Levenberg-Marquardt）算法优化的 BP (Back Propagation)神经网络的多级往复式压缩机压缩机气阀故障诊断方法。以6M25-185/314氢氮气压缩机的 6级压差和6级温差作为网络的输入向量，建立可对往复式压缩机一至六级气阀故障进行在线监测及故障诊断的LM-BP神经网络模型。以100组故障数据作为网络训练样本，30组数据作为网络检测样本进行故障诊断，结果表明，LM-BP神经网络相比于变梯度BP神经网络和RBF神经网络诊断更快速稳定且准确率达到96%以上。利用Matlab软件平台建立的LM-BP 神经网络故障诊断模型，模型简单便于在工程实际中应用。

Fault Diagnosis Method for Compressor Valve Based on LM-BP Neural Network

It proposes a fault diagnosis method of multi-stage reciprocating compressor valve based on LM (Levenberg-Marquardt) algorithm to optimize the BP (Back Propagation) neural network. Six-level pressure differences and six-stage temperature differences of 6M25-185/314 hydrogen nitrogen compressor regarded as the input vector of the network, to establish the LM-BP neural network model which can be used in online monitoring and fault diagnosis of the one-to-six level valve fault of the reciprocating compressor. 100 groups of fault data as the network training samples and 30 sets of data as the network detection samples for fault diagnosis, the results show that, compared to the variable gradient BP neural network and RBF neural network, LM-BP neural network is more rapid and more stable and the accuracy rate of diagnosis reaches above 96%. Built by using the Matlab software platform, fault diagnosis model of LM-BP neural network is simple and can be easily used in engineering practice.