Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

• Autorzy: Zhang Yongfei , Wang Jiaxuan , Zhao Rongzhen , Deng Linfeng

Identyfikatory

DOI

10.17531/ein/177417

• Języki publikacji: EN

Abstrakty

EN

Traditional dimensionality reduction techniques usually rely on a single or a limited number of similar graphs for graph embedding, which limits their ability to extract more information about the internal structure of the data. To address this problem, this study proposes a rotor fault dataset dimensionality reduction algorithm based on multi-class graph joint embedding (MCGJE). The algorithm first overcomes the defect that the traditional feature space cannot take both local and global information into account by constructing local and global median feature line graphs; secondly, based on the graph embedding framework, the algorithm also constructs a hypergraph structure for inscribing complex multivariate relationships between high-dimensional data in the feature space, which in turn enables it to contain more fault information. Finally, we conducted two different rotor fault simulation experiments. The results show that the MCGJE-based algorithm has robustdimensionality reduction capability and can significantly improve the accuracy of fault identification.

Słowa kluczowe

EN

Multi-class graph; dimensionality reduction; hypergraph structure; median feature line graph

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 1
• Strony: art. no. 177417
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Zhang Yongfei

• School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050,People’s Republic of China

• https://orcid.org/0009-0005-6838-7648

autor

Wang Jiaxuan

• School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050,People’s Republic of China

• https://orcid.org/0009-0008-0450-6857

autor

Zhao Rongzhen

• School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050,People’s Republic of China

• https://orcid.org/0000-0001-9225-8700

autor

Deng Linfeng

• School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050,People’s Republic of China

• https://orcid.org/0000-0001-8813-4692

Bibliografia

• 1. Alshingiti Z, Alaqel R, Al-Muhtadi J, Haq QEU, Saleem K, Faheem MH. A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN. Electronics. 2023; 12(1): 232. https://doi.org/10.3390/electronics12010232.
• 2. Banach R, Jeske C. Simple feature engineering via neat default retrenchments. Journal of Logic and Algebraic Programming. 2011; 80(8): 453-80. https://doi.org/10.1016/j.jlap.2010.12.001.
• 3. Chen F, Cheng M, Tang B, Chen B, Xiao W. Pattern recognition of a sensitive feature set based on the orthogonal neighborhood preserving embedding and adaboost_SVM algorithm for rolling bearing early fault diagnosis. Measurement Science and Technology. 2020; 31(10). https://doi.org/10.1088/1361-6501/ab8c11.
• 4. Chen Y-N. Multiple Kernel Feature Line Embedding for Hyperspectral Image Classification. Remote Sensing. 2019; 11(24). https://doi.org/10.3390/rs11242892.
• 5. Dong X, Zhao R, Yuan J, Chen P, He T, Wei K. Dimensionality reduction method based on multiple feature-space collaborative discriminative projection for rotor fault diagnosis. Measurement Science and Technology. 2023; 34(5). https://doi.org/10.1088/1361-6501/acb454.
• 6. Du H, Wang S, Li T. A nearest feature space embedding method based on the combination of nonlinear distance metric and included angle. Computer Engineering and Science. 2018; 40(5): 888-97. http://dx.doi.org/10.3969/j.issn.1007-130X.2018.05.018.
• 7. Gao D, Huang K, Zhu Y, Zhu L, Yan K, Ren Z and Soares C G. Semi-supervised small sample fault diagnosis under a wide range of speed variation conditions based on uncertainty analysis. Reliability Engineering & System Safety. 2024; 242. https://doi.org/10.1016/j.ress.2023.109746.
• 8. Gao D, Zhu Y, Yan K, Fu H, Ren Z, Kang W and Soares C G. Joint learning system based on semi-pseudo-label reliability assessment for weak-fault diagnosis with few labels. Mechanical Systems and Signal Processing. 2023; 189. https://doi.org/10.1016/j.ymssp.2022.110089.
• 9. Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Molecular Diversity. 2021; 25(3): 1315-60. https://doi.org/10.1007/s11030-021-10217-3.
• 10. Kang Y, Chen G, Wang H, Pan W, Wei X. A New Dual-Input Deep Anomaly Detection Method for Early Faults Warning of Rolling Bearings. Sensors (Basel, Switzerland). 2023; 23(18). https://doi.org/10.3390/s23188013.
• 11. Li B, Wang C, Huang D-S. Supervised feature extraction based on orthogonal discriminant projection. Neurocomputing. 2009; 73(1-3): 191-6. https://doi.org/10.1016/j.neucom.2008.09.030.
• 12. Li X, Su K, He Q, Wang X, Xie Z. Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism. Eksploatacja I Niezawodnosc-Maintenance and Reliability. 2023; 25(2). https://doi.org/10.17531/ein/162937.
• 13. Li Y, Feng C. A nonlinear method for monitoring industrial process. Transactions of the Institute of Measurement and Control.2021; 43(2): 400-11. https://doi.org/10.1177/0142331220959232.
• 14. Lyu Y, Zhang Q, Chen A, Wen Z. Interval Prediction of Remaining Useful Life based on Convolutional Auto-Encode and Lower Upper Bound Estimation. Eksploatacja I Niezawodnosc-Maintenance and Reliability. 2023; 25(2). https://doi.org/10.17531/ein/165811.
• 15. Peng B, Wan S, Bi Y, Xue B, Zhang M. Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis. Ieee Transactions on Cybernetics. 2021; 51(10): 4909-23. https://doi.org/10.1109/tcyb.2020.3032945.
• 16. Segovia Ramirez I, Mohammadi-Ivatloo B, Garcia Marquez FP. Alarms management by supervisory control and data acquisition system for wind turbines. Eksploatacja I Niezawodnosc-Maintenance and Reliability. 2021; 23(1): 110-6. https://doi.org/10.17531/ein.2021.1.12.
• 17. Shao H, Xia M, Han G, Zhang Y, Wan J. Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images. Ieee Transactions on Industrial Informatics. 2021; 17(5): 3488-96. https://doi.org/10.1109/tii.2020.3005965.
• 18. Shi M, Zhao R. A Method of Mechanical Fault Diagnosis Based on Locality Margin Discriminant Projection. Journal of Vibration,Measurement and Diagnosis. 2021; 41(1): 126-32. http://dx.doi.org/10.16450/j.cnki.issn.1004-6801.2021.01.018.
• 19. Shi X, He S, Xie X, Sun Y. Review on Feature Extraction of Lubrication and Wear Fault Diagnosis in Tribology System. Tribology. 2023; 43(3): 241-55. http://dx.doi.org/10.16078/j.tribology.2021066.
• 20. Shu X, Zhang S, Li Y, Chen M. An anomaly detection method based on random convolutional kernel and isolation forest for equipment state monitoring. Eksploatacja I Niezawodnosc-Maintenance and Reliability. 2022; 24(4): 758-70. https://doi.org/10.17531/ein.2022.4.16.
• 21. Sun S, Przystupa K, Wei M, Yu H, Ye Z, Kochan O. Fast bearing fault diagnosis of rolling element using Levy Moth-Flame optimization algorithm and Naive Bayes. Eksploatacja I Niezawodnosc-Maintenance and Reliability. 2020; 22(4): 730-40. https://doi.org/10.17531/ein.2020.4.17.
• 22. Wang D, Zhang D, Tang M, Zhang H, Sun T, Yang C, et al. Ethylene chlorotrifluoroethylene/hydrogel-based liquid-solid triboelectric nanogenerator driven self-powered MXene-based sensor system for marine environmental monitoring. Nano Energy. 2022;100. https://doi.org/10.1016/j.nanoen.2022.107509.
• 23. Wang G, Shang G, Pu P, Li X, Peng H. Fake Review Identification Methods Based on Multidimensional Feature Engineering. MobileInformation Systems. 2022; 2022. https://doi.org/10.1155/2022/5229277.
• 24. Wang J, Gao D, Zhu Y, Ren Z, Zhao R, Lin T, et al. Match-reinforcement learning with time frequency selection for bearing fault diagnosis. Measurement Science and Technology. 2023; 34(12). https://doi.org/10.1088/1361-6501/ace644.
• 25. Wang J, Ye L, Gao RX, Li C, Zhang L. Digital Twin for rotating machinery fault diagnosis in smart manufacturing. International Journal of Production Research. 2019; 57(12): 3920-34. https://doi.org/10.1080/00207543.2018.1552032.
• 26. Yan X, Liu Y, Zhang C-a. Multiresolution Hypergraph Neural Network for Intelligent Fault Diagnosis. Ieee Transactions on Instrumentation and Measurement. 2022; 71. https://doi.org/10.1109/tim.2022.3212532.
• 27. Yang C, Ma S, Han Q. Robust discriminant latent variable manifold learning for rotating machinery fault diagnosis. Engineering Applications of Artificial Intelligence. 2023; 126. https://doi.org/10.1016/j.engappai.2023.106996.
• 28. Yuan J, Zhao R, He T, Chen P, Wei K, Xing Z. Fault diagnosis of rotor based on Semi-supervised Multi-Graph Joint Embedding. Isa Transactions. 2022; 131: 516-32. https://doi.org/10.1016/j.isatra.2022.05.006.
• 29. Zhang J, Yin Z, Chen P, Nichele S. Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion. 2020; 59: 103-26. https://doi.org/10.1016/j.inffus.2020.01.011.
• 30. Zhang K, Li H, Cao S, Yang C, Sun F, Wang Z. Motor current signal analysis using hypergraph neural networks for fault diagnosis of electromechanical system. Measurement. 2022; 201. https://doi.org/10.1016/j.measurement.2022.111697.
• 31. Zhao X, Jia M. Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis. Neurocomputing. 2018; 315: 447-64. https://doi.org/10.1016/j.neucom.2018.07.038.
• 32. Zhao X, Yao J, Deng W, Jia M and Liu Z. Normalized Conditional Variational Auto-Encoder with adaptive Focal loss for imbalanced fault diagnosis of Bearing-Rotor system. Mechanical Systems and Signal Processing. 2022; 170. https://doi.org/10.1016/j.ymssp.2022.108826.
• 33. Zhong G, Zhang K, Wei H, Zheng Y, Dong J. Marginal Deep Architecture: Stacking Feature Learning Modules to Build Deep Learning Models. Ieee Access. 2019; 7: 30220-33. https://doi.org/10.1109/access.2019.2902631.
• 34. Zhu X, Zhao X, Yao J, Deng W, Shao H and Liu Z. Adaptive Multiscale Convolution Manifold Embedding Networks for Intelligent Fault Diagnosis of Servo Motor-Cylindrical Rolling Bearing Under Variable Working Conditions. Ieee-Asme Transactions on Mechatronics. 2023. https://doi.org/10.1109/tmech.2023.3314215.