摘要
针对现代储运过程管道堵塞故障诊断时,提取的过程参数多导致诊断速度慢、性能差等问题,提出了基于主成分分析(PCA)和径向基函数(RBF)神经网络故障的诊断方法。首先利用PCA方法对储运过程高维历史数据矩阵进行特征提取,提取的故障特征信息作为训练集,并给出故障特征信息的分类号;然后将其作为RBF神经网络分类器的输入输出进行故障模式识别。仿真实验表明:该方法应用于储运过程管道堵塞故障诊断,不仅大幅度地降低了诊断模型的训练时间,而且提高了诊断正确率。
Many process parameters are selected to diagnose modern storage and transportation process blockage in pipelines, which makes diagnosis slow and its performance poor. To address it, a fault diagnosis method based on principal component analysis（PCA）and radical basis function（RBF）network was proposed. Firstly, PCA method was used to extract the feature of high historical data, as the training set. And the classification of the fault characteristic information was given and then the input and output of RBF network classifiers were taken to identify the failure mode. The simulation results show that the method in pipeline of transportation jam fault diagnosis not only greatly reduces the diagnostic model training time but also improves the diagnostic accuracy.
出处
《后勤工程学院学报》
2014年第3期91-96,共6页
Journal of Logistical Engineering University
