改进LPCDA算法及其在旋转机械故障诊断中的应用  

Improved LPCDA Algorithm and Its Application in Fault Diagnosis of Rotating Machinery

作　　者：薛勇 赵荣珍[1] XUE Yong;ZHAO Rongzhen(School of Mechanical&Electronic Engineering,Lanzhou University of Technology,Lanzhou,730050,China)

机构地区：[1]兰州理工大学机电工程学院,兰州730050

出　　处：《振动．测试与诊断》2023年第1期132-138,202,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51675253);兰州理工大学红柳一流学科建设资助项目。

摘　　要：针对高维故障数据集中有效信息利用率低导致故障分类难度偏大的问题,提出一种线性主成分判别分析(linear principal component discriminant analysis,简称LPCDA)的故障数据集降维算法。该算法将类间可分性判别与主成分计算的思想融入线性判别分析(linear discriminant analysis,简称LDA)算法中,使算法拥有剔除相关信息和冗余特征的能力,从而可以更好地保留能够反映机械运行状态有价值的故障状态信息以及特征的主要成分。实验结果表明,本算法能够剔除高维故障数据集中的相关信息、冗余特征并保留特征主要成分,具有降低故障分类难度与提高自动辨识准确率的功能。该研究可为有效降低高维故障数据集的规模和故障的分类难度、提高有效信息的挖掘能力,提供了理论参考依据。Aiming at the difficulty of fault classification caused by the low utilization rate of effective information in highdimensional fault datasets, linear principal component discriminant analysis(LPCDA) is proposed to reduce the dimension of the fault datasets. The feature of this method is to integrate the idea of discriminability between classes and principal component calculation into the linear discriminant analysis(LDA) algorithm. Through these two ideas, the algorithm has the ability to eliminate relevant information and redundant features. Thus the valuable fault state information and the main components of the characteristics that can reflect the running state of the machine can be retained better. The algorithm has the characteristics of eliminating the relevant information, redundancy feature and the main components of the retained feature in the high-dimensional fault datasets, which has the function of reducing the difficulty of fault classification and improving the accuracy of automatic identification. This research can provide a theoretical reference for effectively reducing the scale of high-dimensional fault datasets, improving the ability to mine effective information and reducing the difficulty of fault classification.

关 键 词：线性主成分判别分析 线性判别分析 可分性 降维 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH165