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

[1]

SU Zuqiang, TANG Baoping, LIU Ziran, Et al., Fault diagnosis method based on orthogonal semi-supervised local fisher discriminant analysis, Chinese Journal of Mechanical Engineering, 50, 18, pp. 7-13, (2014)

[2]

SU Z Q, TANG B P, MA J H, Et al., Fault diagnosis method based on incremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier, Measurement, 48, 1, pp. 136-148, (2014)

[3]

LI Lingjun, HAN Jie, LI Pengyong, Et al., Intelligent fault diagnosis method based on vector-bispectrum, Journal of Mechanical Engineering, 47, 11, pp. 64-68, (2011)

[4]

HE X F, YAN S C, HU Y X, Et al., Face recognition using laplacianfaces, IEEE Transactions on Pattern Analysis & Machine Intelligence, 27, 3, pp. 328-340, (2005)

[5]

BELKIN M, NIYOGI P., Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation, 15, 6, pp. 1373-1396, (2003)

[6]

MARTINEZ A M, KAK A C., PCA versus LDA, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 2, pp. 228-233, (2001)

[7]

GAO Yunlong, WANG Zhihao, DING Liu, Et al., Dynamic weighted nonparametric discriminant analysis, Control and Decision, 35, 8, pp. 1866-1872, (2020)

[8]

YU W W, TENG X L, LIU C Q., Face recognition using discriminant locality preserving projections, Image and Vision Computing, 24, 3, pp. 239-248, (2006)

[9]

ZHANG Xiaotao, TANG Liwei, WANG Ping, Et al., Fault identification and dimensionality reduction method based on semi-supervised PCA-LPP manifold learning algorithm, Journal of Central South University (Natural Science Edition), 47, 5, pp. 1559-1564, (2016)

[10]

SU Zuqiang, TANG Baoping, YAO Jinbao, Fault diagnosis based on sensitive feature selection and manifold learning dimension reduction, Journal of Vibration and Shock, 33, 3, pp. 70-75, (2014)

← 1 2 →