基于半监督邻域自适应正交判别投影的转子故障诊断

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Abstract A rotor fault diagnosis method based on semi-supervised neighborhood adaptive orthogonal discriminant projection (SSNA-ODP) was proposed to solve the difficulty in fault identification due to the high dimension of the fault feature set.The features in time domain, frequency domain and time frequency domain of the original vibration signal were extracted to construct a feature set in mixed domain.The SSNA-ODP method was used to reduce the dimension of the feature set in mixed domain and extract the low-dimensional feature subset which was beneficial to classification and then the set was input into a support vector machine (SVM) for pattern recognition.The application verification by using typical fault data samples shows that the method can improve the generalization ability of the ODP method when there are fewer labeled samples and make use of the data manifold characteristics of global uniform neighborhood parameters, so as to effectively improve the accuracy of fault identification.

Key words： dimension reduction semi-supervised orthogonal discriminant projection(ODP) fault diagnosis

Received: 17 February 2020 Published: 28 May 2021

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	Articles by authors
	CHANG Shuyuan
	ZHAO Rongzhen
	SHI Mingkuan

Cite this article:

CHANG Shuyuan,ZHAO Rongzhen,SHI Mingkuan. Rotor fault diagnosis based on semi-supervised neighborhood adaptiveorthogonal discriminant projection[J]. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(10): 159-165.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2021/V40/I10/159

［1］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(4): 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.

［2］杜伟, 房立清, 齐子元.基于独立特征选择与流形学习的故障诊断［J］.振动与冲击, 2018, 37(16): 77-82.
DU Wei, FANG Liqing, QI Ziyuan.Fault diagnosis based on individual feature selection and manifold learning［J］.Journal of Vibration and Shock, 2018, 37(16): 77-82.

［3］TURK M, PENTLAND A.Eigenfaces for recognition［J］.Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.

［4］HE X, PARTHA N.Locality preserving projections［C］∥ Proceedings of the 17th Annual Conference on Neural Information Processing Systems.Vancouver: NIPS, 2003.

［5］杨乐.基于半监督邻域自适应正交局部保持映射的故障诊断［J］.机械强度, 2018, 40(4): 785-789.
YANG Le.Fault diagnosis based on semi-supervised orthogonal neighborhood adaptive locality preserving projections［J］.Journal of Mechanical Strength, 2018, 40(4): 785-789.

［6］房立清, 吕岩, 张前图, 等.基于半监督邻域自适应LLTSA算法的故障诊断［J］.振动与冲击, 2017, 36(13): 189-194.
FANG Liqing, L Yan, ZHANG Qiantu, et al.Fault diagnosis based on semi-supervised neighborhood adaptive LLTSA［J］.Journal of Vibration and Shock, 2017, 36(13): 189-194.

［7］LI B, WANG C, HUANG D S.Supervised feature extraction based on orthogonal discriminant projection［J］.Neurocomputing, 2008, 73(1): 191-196.

［8］苏祖强, 汤宝平, 邓蕾, 等.有监督LLTSA特征约简旋转机械故障诊断［J］.仪器仪表学报, 2014, 35(8): 1766-1771.
SU Zuqiang, TANG Baoping, DENG Lei, et al.Rotating machinery fault diagnosis with supervised-linear local tangent space alignment for dimension reduction［J］.Chinese Journal of Scientific Instrument, 2014, 35(8):1766-1771.

［9］刘畅, 伍星, 刘韬, 等.基于近似等距投影和支持向量机的滚动轴承故障诊断［J］.振动与冲击, 2018, 37(5): 234-239.
LIU Chang, WU Xing, LIU Tao, et al.Fault diagnosis of rolling bearings based on near-isometric projection and support vector machine［J］.Journal of Vibration and Shock, 2018, 37(5): 234-239.

［10］HINTON G, ROWEIS S.Stochastic neighbor embedding［J］.Advances in Neural Information Processing Systems, 2003, 15(3): 833-840.

［11］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.

［12］韩萍, 韩宾宾.基于典型散射差异指数的PolSAR图像Lee滤波［J］.系统工程与电子技术, 2018, 40(2): 287-294.
HAN Ping, HAN Binbin.Lee filter of PolSAR image based on typical scattering difference index［J］.Systems Engineering and Electronics, 2018, 40(2): 287-294.

［13］赵荣珍, 李超, 张优云.中值与小波消噪集成的转子振动信号滤波方法研究［J］.振动与冲击, 2005, 24(4): 74-77.
ZHAO Rongzhen, LI Chao, ZHANG Youyun.Filter design synthesizing median filtering and wavelet algorithm to de-nose vibration signal polluted by violent pulse noises［J］.Journal of Vibration and Shock, 2005, 24(4): 74-77.

［14］王泽杰, 胡浩民.流形学习算法中的参数选择问题研究［J］.计算机应用与软件, 2010, 27(6): 84-85.
WANG Zejie, HU Haomin.On parameter selection in manifold learning algorithm［J］.Computer Applications and Software, 2010, 27(6): 84-85.