基于局部主成分保持投影的旋转机械故障数据降维方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (6) : 24-30.

论文

原健辉，赵荣珍，马驰

作者信息 +

Dimension reduction method for rotating machinery fault data based on local principal component preserving projection

YUAN Jianhui，ZHAO Rongzhen，MA Chi

Author information +

文章历史 +

摘要

针对旋转机械高维故障特征集存在的特征冗余导致的分类困难问题，提出一种基于局部主成分保持投影(Locality Principal Component Preserving Projection，LPCPP)的故障数据集降维算法。该算法将类间可分性判据、主成分计算两种思想与局部保持投影(Locality Preserving Projection，LPP)相融合，使得算法具有剔除冗余特征、减小降维盲目性的能力，从而可以更好地保留能够反映机械运行状态的高价值密度的故障信息以及特征的主要成分。通过两个不同型号的双跨度转子系统的振动信号对所提算法进行验证，并分别以可分性指标和三种不同分类器的识别准确率对本算法的降维性能进行评价。结果表明，本算法能够达到降低故障分类难度与提高故障分类准确率的功能，其可为积累高价值密度的数据资源和基于“工业大数据”的旋转机械智能决策技术工程实现，提供一种数据运算的理论依据。

Abstract

Aiming at the classification difficulty caused by feature redundancy in high-dimensional fault feature set of rotating machinery, a dimension reduction algorithm of fault data set based on Local Principal Component Preserving Projection (LPCPP) is proposed. The algorithm integrates two ideas of inter-class separability criterion and principal component calculation with Locality Preserving Projection (LPP), which makes the algorithm have the ability to eliminate redundant features and reduce dimensionality reduction blindness, so that it can better retain the fault information with high value density and the main components of features that can reflect the operation status of machinery. The proposed algorithm is verified by the vibration signals of two different types of two span rotor systems, and the dimensionality reduction performance of the algorithm is evaluated by the separability index and the recognition accuracy of three different classifiers. The results show that the algorithm can achieve the function of reducing the difficulty of fault classification and improving the accuracy of fault classification, which can provide a theoretical basis for data computing to accumulate the data resources with high value density and the engineering implementation of intelligent decision-making technology for rotating machinery based on "industrial big data".

导出引用

原健辉，赵荣珍，马驰. 基于局部主成分保持投影的旋转机械故障数据降维方法[J]. 振动与冲击, 2023, 42(6): 24-30

YUAN Jianhui，ZHAO Rongzhen，MA Chi. Dimension reduction method for rotating machinery fault data based on local principal component preserving projection[J]. Journal of Vibration and Shock, 2023, 42(6): 24-30

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