Research on Online Condition monitoring for Complex System based on Modified Broad Learning Systems

• Autorzy: Wang Chong , Liu Jie

Identyfikatory

DOI

10.17531/ein/183600

• Języki publikacji: EN

Abstrakty

EN

Complex systems contain numerous interacting components, thus deep learning methods with powerful performance and complex structure are often used to achieve condition monitoring. However, the deep learning methods are always too time-consuming and hardware-demanding to be loaded into complex systems for online training and updates. To achieve accurate and timely monitoring of complex system state, based on broad learning system (BLS), an online condition monitoring method is proposed in this paper. GeneralBLSs are based on a randomly generated hidden-layer, usually perform poorly in high-dimensional data classification tasks. In this work, based on correlation and causality, two modified BLSs are proposed and mixed to establish the online monitoring system. Specifically, logistic regression (LR) and structural causal model (SCM) are considered to form rough predictions of the system state, thus to replace the randomly generated ones with no practical significance. The effectiveness of the proposed online monitoring method is verified by both simulation data and real data.

Słowa kluczowe

EN

broad learning system; causality; complex system; condition monitoring; high-speed train

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2024
• Tom: Vol. 26, no. 2
• Strony: art. no. 183600
• Opis fizyczny: Bibliogr. 34 poz., rys., tab., wykr.

Twórcy

autor

Wang Chong

• Beihang University, China

• https://orcid.org/0000-0002-4286-4902

autor

Liu Jie

• Beihang University, China

• https://orcid.org/0000-0003-0895-7598

Bibliografia

• 1. Chavan N, Kale M, Deshmukh S, et al.A Review on Development and Trend of Condition Monitoring and Fault Diagnosis[J].ECS transactions,2022,107(1):17863-17870.DOI: 10.1149/10701.17863ecst
• 2. Chen CLP, Liu Z. Broad learning system: A new learning paradigm and system without going deep[C]// 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2017.DOI: 10.1109/YAC.2017.7967609.
• 3. Chen CLP, Liu Z. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1):10-24.DOI: 10.1109/TNNLS.2017.2716952.
• 4. Chen H, Jiang B. AReview of Fault Detection and Diagnosis for the Traction System in High-Speed Trains,IEEE Transactions on Intelligent Transportation Systems, 2020, 21(2):450-465.DOI: 10.1109/TITS.2019.2897583.
• 5. Chen H, Wang T, Chen T, et al. Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network[J]. Remote Sensing, 2023, 15(13):3402. DOI:10.3390/rs15133402.
• 6. David W H, Stanley L.Applied Logistic Regression[M], second edition.Wiley Press, 2005.DOI:10.1002/0471722146.
• 7. Du J, Vong C M, Chen CLP. Novel Efficient RNN and LSTM-Like Architectures: Recurrent and Gated Broad Learning Systems and Their Applications for Text Classification[J]. IEEE Transactions on Cybernetics, 2021, PP(99):1-12.DOI: 10.1109/TCYB.2020.2969705
• 8. Glymour C,Zhang K, Spirtes P.Review of Causal Discovery Methods Based on Graphical Models[J]. Frontiers in Genetics, 2019, 10:524-538.DOI: 10.3389/fgene.2019.00524.
• 9. Gong X, Zhang T,Chen CLP, et al. Research Review for Broad Learning System: Algorithms, Theory, and Applications[J]. IEEE Transactions on Cybernetics, 2021, PP(99):1-29.DOI: 10.1109/TCYB.2021.3061094.
• 10. Ji A, Woo W L, Wong W L E, et al. Rail track condition monitoring: a review on deep learning approaches[J].Intelligence & Robotics, 2021, 1(2):151-175.DOI:10.20517/ir.2021.14.
• 11. Jiang S B, Wong P K, Liang Y. A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition[J]. IEEE Access, 2019, 7:157796-157806. DOI: 10.1109/ACCESS.2019.2950240.
• 12. Kong W, Qiu M, Li M, et al. Causal Graph Convolutional Neural Network For Emotion Recognition[J]. IEEE Transactions on Cognitive and Developmental Systems, 2022, PP(99):1-1. DOI:10.1109/TCDS.2022.3175538.
• 13. Kostrzewski M, Melnik R. Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review[J]. Sensors, 2021, 21(14):4710. DOI:10.3390/s21144710.
• 14. Kou L, Qin Y, Zhao X, et al. A Multi-dimension End-to-End CNN Model for Rotating Devices Fault Diagnosis on High Speed Train Bogie[J].IEEE Transactions on Vehicular Technology, 2020, 69(3):2513-2524.DOI:10.1109/TVT.2019.2955221
• 15. Li J, Shi J. Knowledge discovery from observational data for process control using causal Bayesian networks[J]. Iie Transactions, 2007, 39(6):681-690. DOI:10.1080/07408170600899532.
• 16. Li T, Fang B, Qian J, et al.CNN-Based Broad Learning System[C]//2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), Wuxi, China, 2019:132-136, DOI: 10.1109/SIPROCESS.2019.8868769.
• 17. Li Xn, Zhao H, Deng W. BFOD: Blockchain-based Privacy Protection and Security Sharing Scheme of Flight Operation Data[J]. IEEE Internet of Things Journal, 2023, PP:1-1, DOI:10.1109/JIOT.2023.3296460.
• 18. Marloes H M, Preetam N. A review of some recent advances in causal inference[J]. arXiv, 2015.DOI:10.48550/arXiv.1506.07669.
• 19. Maurya S, Singh V, Verma N K. Condition Monitoring of Machines using Fused Features from EMD based Local Energy with DNN[J].IEEE Sensors Journal, 2019, PP(99):1-1. DOI:10.1109/JSEN.2019.2927754.
• 20. PearlJ. Causality: Models, Reasoning, and Inference [M], second edition. Cambridge University Press, 2000.DOI:10.1111/1468-0297.13919.
• 21. PearlJ. Graphs, Causality, and Structural Equation Models[J]. Sociological Methods & Research, 1998, 27(2):226-284.DOI: 10.1177/0049124198027002004.
• 22. Spirtes P,Clark G. An Algorithm for Fast Recovery of Sparse Causal Graphs[J]. Social Science Computer Review, 1991:62-72.DOI: 10.1177/089443939100900106.
• 23. Tang R, De D L, Besinovic N, et al. A literature review of Artificial Intelligence applications in railway systems[J]. Transportation research Part C: Emerging technologies, 2022, Jul.(140):103679.1-103679.25. DOI:10.1016/j.trc.2022.103679.
• 24. Wang S, Xiang J. A minimumentropydeconvolution-enhancedconvolutionalneuralnetworksforault diagnosis of axial piston pumps[J]. Soft Computing, 2020, 24(20): 2983-2997.DOI: 10.1007/s00500-019-04076-2.
• 25. Wang S, Xiang J, Zhong Y, et al. A data indicator-based deep belief networks to detect multiple faults in axial piston pumps[J]. Mechanical Systems and Signal Processing, 2018, 112:154-170. DOI: 10.1016/j.ymssp.2018.04.038.
• 26. Wei S, Li J, Zhao Z, et al. Artificial Monitoring of Eccentric Synchronous Reluctance Motors Using Neural Networks[J].Computers, Materials, & Continua, 2022, 000(007):1035-1052.DOI:10.32604/cmc.2022.024201.
• 27. Yang F. A CNN-Based Broad Learning System[C]// 2018 IEEE 4th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2018:2105-2109. DOI: 10.1109/CompComm.2018.8780984.
• 28. Zhang J. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias[J]. Artificial Intelligence, 2008, 172(16-17):1873-1896.DOI:10.1016/j.artint.2008.08.001.
• 29. Yu X, Tang B, Zhang K. Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70:1-14. DOI:10.1109/TIM.2020.3048799
• 30. Zhang Y, Qin N, Huang D,et al.Fault Diagnosis of High-speed Train Bogie Based on Deep Neural Network[J].SCIENCE CHINA Information Sciences, 2021, 52(24):135-139.DOI:10.1016/j.ifacol.2019.12.395.
• 31. Zhao B, Zhang X, Zhan Z, et al. A robust construction of normalized CNN for online intelligent condition monitoring of rolling bearings considering variable working conditions and sources[J]. Measurement, 2021, 174:108973.1-108973.16.DOI: 10.1016/j.measurement.2021.108973.
• 32. Zhao H, Zheng J, Xu J,et al.Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System[J].IEEE Access, 2019,7:99263-99272.DOI: 10.1109/ACCESS. 2019.2929094.
• 33. Zheng W, Yuan Q, Zou L, et al. Study on a Novel Fault Diagnosis Method Based on VMD and BLM[J]. Symmetry,2019, 11(6):747-766.DOI: 10.3390/sym11060747.
• 34. Zhu L, Zhou Y, Jia R, et al. Real-Time Fault Diagnosis for EVs With Multilabel Feature Selection and Sliding Window Control[J]. IEEE internet of things journal, 2022, 19(9):18346-18359. DOI: 10.1109/JIOT.2022.3160298.