基于SILPDA的旋转机械故障诊断方法

Abstract
Figure/Table
References (13)
Related Citation (15)

Download: PDF (1376 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Aiming at the difficulty of fault classification caused by "dimension disaster" in fault feature set, a dimension reduction algorithm of fault feature set based on enhanced intrinsic local preserving discriminant analysis (SILPDA) is proposed. The algorithm integrates the enhanced multi manifold intrinsic model and local similarity matrix into the construction of the objective function. During this period, the multi manifold structure characteristics of the data set are fully considered and the local structure information of the sample is retained, so that the low dimensional feature subset after dimensionality reduction is easy to implement classification operation, and then achieve the effect of improving the accuracy of fault identification. The performance of the algorithm is verified by using the original fault feature set constructed from the vibration signal set of the rotor test-bed. The results show that the algorithm can extract sensitive feature subsets that are conducive to the implementation of classification operation from the original fault data set. These feature subsets will make the boundary between different fault categories clearer. Finally, compared with "LPP, LDA, LMDP" and other algorithms, the algorithm can achieve better fault identification effect. For improving the value density of rotating machinery big data resources, this algorithm provides a theoretical basis for optimizing the data structure model.

Key words： Fault diagnosis Dimension reduction Intrinsic structure Multiple manifold

Received: 30 December 2021 Published: 28 January 2023

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	DONG Xiaoxin
	ZHAO Rongzhen

Cite this article:

DONG Xiaoxin,ZHAO Rongzhen. Fault diagnosis method of rotating machinery based on SILPDA[J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(2): 16-22.

URL:

http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2023/V42/I2/16

[1] 苗强, 蒋京, 张恒, 等. 工业大数据背景下的航空智能发动机:机遇与挑战[J]. 仪器仪表学报, 2019, 40(07): 1-12.
MIAO Qiang, JIANG Jing, ZHANG Heng, et al. Development of aviation intelligent engine under industrial big data: Chances and challenges[J]. Chinese Journal of Scientific Instrument, 2019, 40(07): 1-12.
[2] 赵孝礼, 赵荣珍. 全局与局部判别信息融合的转子故障数据集降维方法研究[J]. 自动化学报, 2017, 43(04): 560-567.
ZHAO Xiaoli，ZHAO Rongzhen. A method of dimension reduction of rotor faults data set based on fusion of global and local discriminant information[J]. Acta Automatica Sinica, 2017，43(4): 560-567.
[3] MARTINEZ A M, KAK A C. Pca versus lda[J]. IEEE transactions on pattern analysis and machine intelligence, 2001, 23(2): 228-233.
[4] 高云龙, 王志豪, 丁柳, 等. 动态加权非参数判别分析[J]. 控制与决策, 2020, 35(8): 1866-1872.
GAO Yunlong, WANG Zhihao, DING Liu, et al. Dynamic weighted nonparametric discriminant analysis［J］. Control and Decision，2020, 35(8): 1866-1872.
[5] 冀丰偲, 余云松, 张早校. LDA＿SVM方法在化工过程故障诊断中的应用[J]. 高校化学工程学报, 2020, 34(02): 487-494.
JI Fengluo, YU Yunsong, ZHANG Zaoxiao. Application of LDA and SVM method in fault diagnosis of chemical process[J]. Journal of Chemical Engineering of Chinese Universities, 2020, 34(02): 487-494.
[6] 石明宽, 赵荣珍. 基于局部边缘判别投影的机械故障诊断方法[J]. 振动.测试与诊断, 2021, 41(01): 126-132+204.
SHI Mingkuan, ZHAO Rongzhen. A method of mechanical fault diagnosis based on locality margin discriminant projection[J]. Journal of Vibration, Measurement & Diagnosis, 2021, 41(01): 126-132+204.
[7] HE X. Locality preserving projections[J]. Advances in Neural Information Processing Systems, 2003, 16(1): 186-197.
[8] GAO L, SONG W, WEN L, et al. An unsupervised fault diagnosis method based on iterative multi-manifold spectral clustering[J]. IET Collaborative Intelligent Manufacturing, 2019, 1(2): 48-55.
[9] WANG Y, WU Y. Face recognition using intrinsicfaces[J]. Pattern Recognition, 2010, 43(10): 3580-3590.
[10] YU W, TENG X, LIU C. Face recognition using discriminant locality preserving projections[J]. Image And Vision Computing, 2006.
[11] TZUONG T M. Linear Algebra And Its Applications[M]. World Scientific Publishing Company， Singapore： 2020.
[12] 苏祖强, 汤宝平, 姚金宝. 基于敏感特征选择与流形学习降维的故障诊断[J]. 振动与冲击, 2014, 33(03): 70-75.
SU Zuqiang, TANG Baoping, YAO Jinbao. Fault diagnosis based on sensitive feature selection and manifold learning dimension reduction[J]. Journal of Vibration and Shock, 2014, 33(03): 70-75.
[13] 李霁蒲, 赵荣珍. 近邻概率距离在旋转机械故障集分类中的应用方法[J]. 振动与冲击, 2018, 37(11): 48-54.
LI Jipu, ZHAO Rongzhen. Application of neighbor probability distance in classification of rotating machinery fault sets[J]. Journal of Vibration and Shock, 2018, 37(11): 48-54.

[1]	. [J]. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(4): 11-.