A Method of Mechanical Fault Diagnosis Based on Locality Margin Discriminant Projection

被引：0

作者：

Shi M. ^{[1
]}

Zhao R. ^{[1
]}

机构：

[1] School of Mechanic & Electrical Engineering, Lanzhou University of Technology, Lanzhou

来源：

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2021年 / 41卷 / 01期

关键词：

Dimensionality reduction; Fault diagnosis; Manifold learning; Rotor system;

D O I：

10.16450/j.cnki.issn.1004-6801.2021.01.018

中图分类号：

学科分类号：

摘要：

A locality margin discriminant projection (LMDP) algorithm is proposed to reduce the dimension of the fault feature set.The algorithm defines the local intra-class similarity and the local inter-class similarity to separate the neighboring samples of different classes and join those in the same class. Then, the time-domain and frequency-domain statistical characteristics of rotor vibration signals are extracted to form the original fault feature set. The LMDP algorithm fuse the feature set to select a low-dimensional sensitive feature subset that contains the most intrinsic information. Finally, the K-nearest neighbor (KNN) classifier trains the subset and classify the faults. The vibration signal seta of two different double-span rotor systems verify the effectiveness of the proposed method. © 2021, Editorial Department of JVMD. All right reserved.

引用

页码：126 / 132

页数：6

共 14 条

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