基于高价值小样本的石化装置旋转机械故障诊断NN模型

NN Model for Fault Diagnosis of Petrochemical Rotating Machinery Based on High-value Small Samples

针对石化装置旋转机械故障特征之间呈现模糊性和耦合性导致故障类型识别难的问题,提出基于高价值小样本的石化装置旋转机械故障诊断NN模型。依据故障特征拟合情况,提取高价值小样本故障特征;运用高价值小样本故障特征建立高效的NN模型,利用梯度搜索技术,使网络的实际输出值和期望输出值的误差最小,达到最佳分类效果。研究结果表明:高价值小样本故障特征的训练数据与测试数据具有高度的一致性,故障类型识别的准确率达到98.3%。该方法应用于石化大机组旋转机械表明方法简单有效,高价值小样本特征提取准确,故障识别能力强,可为石化大机组及其他大型设备旋转机械故障诊断提供指导。

The ambiguity and coupling between the fault characteristics of the rotating machinery in petrochemical,lead to diffi⁃culty of identifying the fault type.A NN model was proposed for fault diagnosis of petrochemical rotating machinery based on high-val⁃ue small samples.According to the original fault feature fitting situation,fault features of high-value small samples were extracted.The high-value small sample fault feature was used to establish an efficient NN model.The gradient search technology was used to minimize the error between the actual output value and the expected output value of the network,to achieve the best classification effect.The re⁃sults show that the training data of high-value small sample fault characteristics are highly consistent with the test data,and the accura⁃cy rate of fault type identification is 98.3%.The method applied to rotating machinery in petrochemical shows that it is simple and ef⁃fective.The high-value small sample features are extracted accurately,and the fault identification ability is strong.It can provide refer⁃ence for fault diagnosis of rotating machinery in petrochemical units and other large equipment.