| Citation: | YANG Changyuan, MA Sai, HAN Qinkai. Fault diagnosis of rotating machinery based on multi-kernel supervised manifold learning[J]. Journal of Aerospace Power, 2024, 39（10）：20220184 doi: 10.13224/j.cnki.jasp.20220184
|
In order to accurately perform fault diagnosis for rotating machinery, a multi-kernel supervised manifold learning (MKSML) algorithm was proposed. More specifically, MKSML algorithm allowed to effectively select the features of high-dimensional fault data, and extract the low-dimensional fault features with better discrimination. Through the idea of supervised learning, the clustering of similar samples and the differences between various samples have been enhanced. A novel weighted neighborhood graph was proposed by constructing multi-kernel function. The distance information and angle information between adjacent points were retained. And the interference of outliers and noise in the sample was suppressed. Through the gray wolf optimization algorithm to adjust the MKSML parameters, the algorithm could be applied to various types of rotating machinery fault diagnosis. The fault diagnosis model of rotating machinery based on MKSML was proposed, and bearing fault diagnosis experiments and gear fault diagnosis experiments were conducted.
| [1] |
DING Han,GAO R X,ISAKSSON A J,et al. State of AI-based monitoring in smart manufacturing and introduction to focused section[J]. IEEE/ASME Transactions on Mechatronics,2020,25(5): 2143-2154. doi: 10.1109/TMECH.2020.3022983
|
| [2] |
雷亚国,许学方,蔡潇,等. 面向机械装备健康监测的数据质量保障方法研究[J]. 机械工程学报,2021,57(4): 1-9. LEI Yaguo,XU Xuefang,CAI Xiao,et al. Research on data quality assurance for health condition monitoring of machinery[J]. Journal of Mechanical Engineering,2021,57(4): 1-9. (in Chinese doi: 10.3901/JME.2021.04.001
LEI Yaguo, XU Xuefang, CAI Xiao, et al. Research on data quality assurance for health condition monitoring of machinery[J]. Journal of Mechanical Engineering, 2021, 57(4): 1-9. (in Chinese) doi: 10.3901/JME.2021.04.001
|
| [3] |
林京. 机器信息学: 机械产品智能化的学科支撑[J]. 机械工程学报,2021,57(2): 11-20. LIN Jing. Machinery informatics: a fundamental discipline to intelligent machinery[J]. Journal of Mechanical Engineering,2021,57(2): 11-20. (in Chinese doi: 10.3901/JME.2021.02.011
LIN Jing. Machinery informatics: a fundamental discipline to intelligent machinery[J]. Journal of Mechanical Engineering, 2021, 57(2): 11-20. (in Chinese) doi: 10.3901/JME.2021.02.011
|
| [4] |
LIU Ruonan,YANG Boyuan,ZIO E,et al. Artificial intelligence for fault diagnosis of rotating machinery: a review[J]. Mechanical Systems and Signal Processing,2018,108: 33-47. doi: 10.1016/j.ymssp.2018.02.016
|
| [5] |
叶壮,余建波. 基于多通道加权卷积神经网络的齿轮箱振动信号特征提取[J]. 机械工程学报,2021,57(1): 110-120. YE Zhuang,YU Jianbo. Feature extraction of gearbox vibration signals based on multi-channels weighted convolutional neural network[J]. Journal of Mechanical Engineering,2021,57(1): 110-120. (in Chinese doi: 10.3901/JME.2021.01.110
YE Zhuang, YU Jianbo. Feature extraction of gearbox vibration signals based on multi-channels weighted convolutional neural network[J]. Journal of Mechanical Engineering, 2021, 57(1): 110-120. (in Chinese) doi: 10.3901/JME.2021.01.110
|
| [6] |
MA Sai,CHU Fulei,HAN Qinkai. Deep residual learning with demodulated time-frequency features for fault diagnosis of planetary gearbox under nonstationary running conditions[J]. Mechanical Systems and Signal Processing,2019,127: 190-201. doi: 10.1016/j.ymssp.2019.02.055
|
| [7] |
侯文擎,叶鸣,李巍华. 基于改进堆叠降噪自编码的滚动轴承故障分类[J]. 机械工程学报,2018,54(7): 87-96. HOU Wenqing,YE Ming,LI Weihua. Rolling element bearing fault classification using improved stacked de-noising auto-encoders[J]. Journal of Mechanical Engineering,2018,54(7): 87-96. (in Chinese doi: 10.3901/JME.2018.07.087
HOU Wenqing, YE Ming, LI Weihua. Rolling element bearing fault classification using improved stacked de-noising auto-encoders[J]. Journal of Mechanical Engineering, 2018, 54(7): 87-96. (in Chinese) doi: 10.3901/JME.2018.07.087
|
| [8] |
董绍江,裴雪武,汤宝平,等. 基于FNER性能退化指标及IDRSN的滚动轴承寿命状态识别方法[J]. 机械工程学报,2021,57(15): 105-115. DONG Shaojiang,PEI Xuewu,TANG Baoping,et al. Recognition of rolling bearing life status based on FNER performance degradation indicator and IDRSN[J]. Journal of Mechanical Engineering,2021,57(15): 105-115. (in Chinese doi: 10.3901/JME.2021.15.105
DONG Shaojiang, PEI Xuewu, TANG Baoping, et al. Recognition of rolling bearing life status based on FNER performance degradation indicator and IDRSN[J]. Journal of Mechanical Engineering, 2021, 57(15): 105-115. (in Chinese) doi: 10.3901/JME.2021.15.105
|
| [9] |
郭庆,李印龙. 基于气路参数融合的涡扇发动机性能退化预测[J]. 航空动力学报,2021,36(11): 2251-2260. GUO Qing,LI Yinlong. Turbofan engine performance degradation prediction based on gas path parameter fusion[J]. Journal of Aerospace Power,2021,36(11): 2251-2260. (in Chinese
GUO Qing, LI Yinlong. Turbofan engine performance degradation prediction based on gas path parameter fusion[J]. Journal of Aerospace Power, 2021, 36(11): 2251-2260. (in Chinese)
|
| [10] |
HE Liang,CHEN Xianhong,XU Can,et al. Local pairwise linear discriminant analysis for speaker verification[J]. IEEE Signal Processing Letters,2018,25(10): 1575-1579. doi: 10.1109/LSP.2018.2869107
|
| [11] |
戚晓利,王振亚,吴保林,等. 基于ACMPE、ISSL-Isomap和GWO-SVM的行星齿轮箱故障诊断[J]. 航空动力学报,2019,34(4): 744-755. QI Xiaoli,WANG Zhenya,WU Baolin,et al. Planetary gearbox fault diagnosis based on ACMPE,ISSL-Isomap and GWO-SVM[J]. Journal of Aerospace Power,2019,34(4): 744-755. (in Chinese
QI Xiaoli, WANG Zhenya, WU Baolin, et al. Planetary gearbox fault diagnosis based on ACMPE, ISSL-Isomap and GWO-SVM[J]. Journal of Aerospace Power, 2019, 34(4): 744-755. (in Chinese)
|
| [12] |
ROWEIS S T,SAUL L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science,2000,290(5500): 2323-2326. doi: 10.1126/science.290.5500.2323
|
| [13] |
BELKIN M,NIYOGI P. Laplacian eigenmaps for dimensionality reduction and data representation[J]. Neural Computation,2003,15(6): 1373-1396. doi: 10.1162/089976603321780317
|
| [14] |
JIANG Quansheng,JIA Minping,HU Jianzhong,et al. Machinery fault diagnosis using su pervised manifold learning[J]. Mechanical Systems and Signal Processing,2009,23(7): 2301-2311. doi: 10.1016/j.ymssp.2009.02.006
|
| [15] |
LI Benwei,ZHANG Yun. Supervised locally linear embedding projection (SLLEP) for machinery fault diagnosis[J]. Mechanical Systems and Signal Processing,2011,25(8): 3125-3134. doi: 10.1016/j.ymssp.2011.05.001
|
| [16] |
SU Zuqiang,TANG Baoping,LIU Ziran,et al. Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine[J]. Neurocomputing,2015,157: 208-222. doi: 10.1016/j.neucom.2015.01.016
|
| [17] |
SAUL L K. A tractable latent variable model for nonlinear dimensionality reduction[J]. Proceedings of the National Academy of Sciences of the United States of America,2020,117(27): 15403-15408.
|
| [18] |
罗林开,叶凌君,周绮凤. 常用核函数的几何度量与几何性质[J]. 厦门大学学报(自然科学版),2009,48(6): 804-807. LUO Linkai,YE Lingjun,ZHOU Qifeng. Geometric measures and properties of commonly used kernel functions[J]. Journal of Xiamen University (Natural Science),2009,48(6): 804-807. (in Chinese
LUO Linkai, YE Lingjun, ZHOU Qifeng. Geometric measures and properties of commonly used kernel functions[J]. Journal of Xiamen University (Natural Science), 2009, 48(6): 804-807. (in Chinese)
|
| [19] |
MIRJALILI S,MIRJALILI S M,LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software,2014,69: 46-61. doi: 10.1016/j.advengsoft.2013.12.007
|
| [20] |
王晓龙,唐贵基,周福成. 自适应可调品质因子小波变换在轴承早期故障诊断中的应用[J]. 航空动力学报,2017,32(10): 2467-2475. WANG Xiaolong,TANG Guiji,ZHOU Fucheng. Application of adaptive tunable Q-factor wavelet transform on incipient fault diagnosis of bearing[J]. Journal of Aerospace Power,2017,32(10): 2467-2475. (in Chinese
WANG Xiaolong, TANG Guiji, ZHOU Fucheng. Application of adaptive tunable Q-factor wavelet transform on incipient fault diagnosis of bearing[J]. Journal of Aerospace Power, 2017, 32(10): 2467-2475. (in Chinese)
|
| [21] |
ZHANG Shiqing. Enhanced supervised locally linear embedding[J]. Pattern Recognition Letters,2009,30(13): 1208-1218. doi: 10.1016/j.patrec.2009.05.011
|
| [22] |
洪骥宇,王华伟,倪晓梅. 基于降噪自编码器的航空发动机性能退化评估[J]. 航空动力学报,2018,33(8): 2041-2048. HONG Jiyu,WANG Huawei,NI Xiaomei. Assessment of performance degradation for aero-engine based on denoising autoencoder[J]. Journal of Aerospace Power,2018,33(8): 2041-2048. (in Chinese
HONG Jiyu, WANG Huawei, NI Xiaomei. Assessment of performance degradation for aero-engine based on denoising autoencoder[J]. Journal of Aerospace Power, 2018, 33(8): 2041-2048. (in Chinese)
|
| [23] |
HAYAKAWA Y,OGATA T,SUGANO S. Flexible assembly work cooperating system based on work state identifications by a self-organizing map[J]. IEEE/ASME Transactions on Mechatronics,2004,9(3): 520-528. doi: 10.1109/TMECH.2004.836253
|
| [24] |
GOPI E S,PALANISAMY P. Maximizing Gaussianity using kurtosis measurement in the kernel space for kernel linear discriminant analysis[J]. Neurocomputing,2014,144: 329-337. doi: 10.1016/j.neucom.2014.05.020
|
| [25] |
SHAO Siyu,MCALEER S,YAN Ruqiang,et al. Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE Transactions on Industrial Informatics,2019,15(4): 2446-2455. doi: 10.1109/TII.2018.2864759
|
DownLoad:
