Dimension reduction method of rotor fault data set based on LRDP

Abstract

Abstract: To improve the poor fault identification precision caused by high-dimensional fault datasets, a multigraph cooperative rotor fault dataset dimensionality reduction strategy integrating local reconstruction and border discrimination is proposed. In order to maintain the intrinsic manifold structure of the fault data, the fault features of rotor vibration signals are extracted first from multiple domains and constructs a high-dimensional fault dataset. Next, each sample is linearly reconstructed using its neighboring samples. Finally, an eigenmap and penalty map,as well as discriminant map of the fault feature dataset are constructed, and dimensionality reduction is performed through a multi-graph embedding model. The algorithm's performance was evaluated using two distinct high-dimensional rotor failure datasets. The results demonstrated that the proposed algorithm surpassed other well-known algorithms in terms of recognition accuracy and stability. It has overall recognition accuracy of 99.7% and 98.7%, respectively.

Key words: fault diagnosis, graph embedding, visualization of data, dimension reduction, local remodeling

CLC Number:

TH165
TP18

LIANG Qi-bo, ZHAO Rong-zhen, DENG Lin-feng. Dimension reduction method of rotor fault data set based on LRDP[J]. Journal of Lanzhou University of Technology, 2026, 52(2): 55-62.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://journal.lut.edu.cn/EN/

https://journal.lut.edu.cn/EN/Y2026/V52/I2/55

References

[1] JIANG F,ZHU Z C,LI W,et al.Fault identification of rotor bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis [J].Journal of Sound and Vibration,2014,333(14):3321-3331.
[2] SU Z Q,TANG B P,MA J H,et al.Fault diagnosis method based onincremental enhanced supervised locally linear embedding and adaptive nearest neighbor classifier [J].Measurement,2014,48(1):136-148.
[3] 雷亚国,贾峰,孔德同,等.大数据下机械智能故障诊断的机遇与挑战 [J].机械工程学报,2018,54(5):94-104.
[4] HE X F,NIYOGI P.Locality preserving projections [J].Advances in Neural Information Processing Systems,2003,16:153-160.
[5] YAN S C,XU D,ZHANG B Y,et al.Graph embedding and extensions:a general framework for dimensionality reduction [J].IEEE Transcations on Pattern Analysis and Machine Intelligence,2007,29(1):40-51.
[6] ZHAO H T,SUN S Y,JING Z L,et al.Local structure based supervised feature extraction [J].Pattern Recognition,2006,39(8):1546-1550.
[7] 石明宽,赵荣珍.基于局部平衡判别投影的旋转机械故障诊断方法 [J].中国机械工程,2021,32(14):1653-1658.
[8] 刘嘉敏,杨松,黄鸿.基于局部重构Fisher分析的高光谱遥感影像分类 [J].中国激光,2020,47(7):390-401.
[9] 常书源,赵荣珍,陈博,等.基于边界判别多流形分析的故障数据集降维方法 [J].振动与冲击,2021,40(23):120-126.
[10] HUANG C B,ABEO T A,LUO X Z,et al.Semi-supervised manifold alignment with multi-graph embedding [J].Multimedia Tools and Applications,2020,79(27/28):20241-20262.
[11] 李勇智,杨静宇,郑宇杰,等.基于最大间距准则(MMC)新的有效特征提取方法 [J].系统仿真学报,2007(5):1061–1066.
[12] 周宏飞,赵荣珍.基于GSRDPGE算法的转子故障数据集降维方法研究 [J].兰州理工大学学报,2023,49(4):35-41.
[13] ZHAO X L,JIA M P.Fault diagnosis of rolling bearing on feature reduction with global-local margin fisher analysis [J].Neurocomputing,2018,315:447-464.
[14] 户文刚,赵荣珍.基于转子故障数据集的KSELF降维方法 [J].振动、测试与诊断,2021,41(1):13-18.

[1]	GUO Jun-feng, CHEN Da-peng, WANG Miao-sheng, WANG Er-hua. Fault diagnosis of gearbox cracks based on variance contribution rate [J]. Journal of Lanzhou University of Technology, 2025, 51(6): 42-48.
[2]	WANG Jiang, YAN Chang-feng, LU Jia-wei, WANG Rui-min, ZHANG Yong-ming. Fault named entity recognition based on multi-headed self-attention mechanism [J]. Journal of Lanzhou University of Technology, 2025, 51(4): 33-42.
[3]	MA Ning, ZHAO Rong-zhen, ZHENG Yu-qiao. Fault diagnosis of rolling bearing based on CEEMDAN combined with improved one-dimensional multi-scale convolutional neural network [J]. Journal of Lanzhou University of Technology, 2025, 51(1): 45-54.
[4]	ZHENG Yu-qiao, LI Hao, WEI Tai. Rolling bearing fault diagnosis of wind turbine based on OVMD-RF method [J]. Journal of Lanzhou University of Technology, 2024, 50(4): 36-42.
[5]	ZHANG Bin, CHENG Hang, MENG Qian. Research on fault diagnosis of rolling bearing based on ICEEMDAN and Hilbert envelope spectrum [J]. Journal of Lanzhou University of Technology, 2024, 50(3): 51-57.
[6]	ZHANG Xiao-ying, SHI Dong-xue, ZHANG Jin, ZHANG Xin. Transient voltage stability assessment of wind power grid connected system based on KPCA feature dimension reduction [J]. Journal of Lanzhou University of Technology, 2024, 50(2): 96-103.
[7]	DU Xian-jun, QIU Xiao-yu. Fault diagnosis for chemical process based on an improved LSTM neural network [J]. Journal of Lanzhou University of Technology, 2023, 49(6): 72-79.
[8]	GUO Jun-feng, SUN Lei, WANG Miao-sheng, XU De-feng. Fault diagnosis method of rolling bearing based on 1D-LeNet-5 model [J]. Journal of Lanzhou University of Technology, 2023, 49(5): 34-41.
[9]	ZHAO Xiao-qiang, LUO Wei-lan, LIANG Hao-peng. Bearing fault diagnosis based on SIR multistage residual connection dense network [J]. Journal of Lanzhou University of Technology, 2022, 48(6): 46-54.
[10]	ZHU Qi-xian, LIANG Hao-peng, ZHAO Xiao-qiang, SONG Zhao-yang. Rolling bearing fault diagnosis method based on residual neural network [J]. Journal of Lanzhou University of Technology, 2022, 48(3): 86-93.
[11]	ZHAO Rong-zhen, XUE Yong, WU Yao-chun. Rolling bearing fault data set dimension reduction method of improved D-t-SNE [J]. Journal of Lanzhou University of Technology, 2022, 48(3): 42-49.
[12]	ZHAO Rong-zhen, CHANG Shu-yuan. Rotor fault diagnosis method based on M-WDLS and PCA [J]. Journal of Lanzhou University of Technology, 2021, 47(5): 38-44.
[13]	ZHAO Rong-zhen, MA Sen-cai, WU Yao-chun. Fault diagnosis method for rolling bearing based on cloud model and ensemble extreme learning machine [J]. Journal of Lanzhou University of Technology, 2021, 47(4): 33-39.
[14]	CHEN Peng, ZHAO Xiao-qiang, ZHU Qi-xian. Fault diagnosis method of rolling bearing based on VMD-MPE -KPCA feature extraction mixed with MRVM [J]. Journal of Lanzhou University of Technology, 2020, 46(5): 92-99.
[15]	YAN Chang-feng, LI Yu, WANG Hui-bin, ZHANG Qiang AI Ke-yong, WU Li-xiao. Knowledge modeling of fault diagnosis for turbine generator sets based on ontology [J]. Journal of Lanzhou University of Technology, 2020, 46(5): 41-48.