REFERENCES

1. Yuan, X.; Wang, Y.; Wang, C.; et al. Variable correlation analysis-based convolutional neural network for far topological feature extraction and industrial predictive modeling. IEEE. Trans. Instrum. Meas. 2024, 73, 1-10.

2. Zhang, J.; Tian, J.; Alcaide, A. M.; et al. Lifetime extension approach based on the levenberg–marquardt neural network and power routing of DC–DC converters. IEEE. Trans. Power. Electron. 2023, 38, 10280-91.

3. Ding, Y.; Ding, P.; Zhao, X.; Cao, Y.; Jia, M. Transfer learning for remaining useful life prediction across operating conditions based on multisource domain adaptation. IEEE/ASME. Transn. Mechatronics. 2022, 27, 4143-52.

4. Yuan, X.; Xu, N.; Ye, L.; et al. Attention-based interval aided networks for data modeling of heterogeneous sampling sequences with missing values in process industry. IEEE. Trans. Ind. Informat. 2023, 20, 5253-62.

5. Ge, Z. Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes. J. Process. Control. 2018, 65, 107-17.

6. Wang, S.; Ju, Y.; Fu, C.; Xie, P.; Cheng, C. A reversible residual network-aided canonical correlation analysis to fault detection and diagnosis in electrical drive systems. IEEE. Trans. Instrum. Meas. 2024, 73, 1-10.

7. Zhao, F.; Jiang, Y.; Cheng, C.; Wang, S. An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization. Meas. Sci. Technol. 2023, 35, 025004.

8. Shang, C.; You, F. Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering 2019, 5, 1010-6.

9. Jiang, Y.; Yin, S.; Dong, J.; Kaynak, O. A review on soft sensors for monitoring, control, and optimization of industrial processes. IEEE. Sensors. J. 2021, 21, 12868-81.

10. Yin, S.; Ding, S. X.; Xie, X.; Luo, H. A review on basic data-driven approaches for industrial process monitoring. IEEE. Trans. Ind. Electron. 2014, 61, 6418-28.

11. Chen, J.; Fan, S.; Yang, C.; Zhou, C.; Zhu, H.; Li, Y. Stacked maximal quality-driven autoencoder: deep feature representation for soft analyzer and its application on industrial processes. Inform. Sci. 2022, 596, 280-303.

12. Åström, K. J.; Bell, R. D. Drum-boiler dynamics. Automatica 2000, 36, 363-78.

13. Lampinen, M.; Laari, A.; Turunen, I. Kinetic model for direct leaching of zinc sulfide concentrates at high slurry and solute concentration. Hydrometallurgy 2015, 153, 160-9.

14. Zamani, M. G.; Nikoo, M. R.; Rastad, D.; Nematollahi, B. A comparative study of data-driven models for runoff, sediment, and nitrate forecasting. J. Environ. Manage. 2023, 341, 118006.

15. Yan, F.; Zhang, X.; Yang, C.; Hu, B.; Qian, W.; Song, Z. Data-driven modelling methods in sintering process: current research status and perspectives. Can. J. Chem. Eng. 2023, 101, 4506-22.

16. Edeh, E.; Lo, W. J.; Khojasteh, J. Review of partial least squares structural equation modeling (PLS-SEM) using R: a workbook. Struct. Equ. Model. 2022, 30, 165-7.

17. Hyvärinen, A.; Khemakhem, I.; Morioka, H. Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning. Patterns 2023, 4, 100844.

18. Song, P.; Zhao, C. Slow down to go better: a survey on slow feature analysis. IEEE. Trans. Neural. Netw. Learn. Syst. 2022, 35, 3416-36.

19. Hardoon, D. R.; Szedmak, S.; Shawe-Taylor, J. Canonical correlation analysis: an overview with application to learning methods. Neural. Comput. 2004, 16, 2639-64.

20. Li, S.; Zheng, Y.; Li, S.; Huang, M. Data-driven modeling and operation optimization with inherent feature extraction for complex industrial processes. IEEE. Trans. Autom. Sci. Eng. 2023, 21, 1092-106.

21. Zhang, Z.; Tao, D. Slow feature analysis for human action recognition. IEEE. Trans. Pattern. Anal. Mach. Intell. 2012, 34, 436-50.

22. Zhou, P.; Lu, S. W.; Chai, T. Data-driven soft-sensor modeling for product quality estimation using case-based reasoning and fuzzy-similarity rough sets. IEEE. Trans. Autom. Sci. Eng. 2014, 11, 992-1003.

23. Zhang, X.; Zou, Y.; Li, S. Enhancing incremental deep learning for FCCU end-point quality prediction. Inform. Sci. 2020, 530, 95-107.

24. Shao, W.; Ge, Z.; Yao, L.; Song, Z. Bayesian nonlinear Gaussian mixture regression and its application to virtual sensing for multimode industrial processes. IEEE. Trans. Autom. Sci. Eng. 2019, 17, 871-85.

25. Yuan, X.; Zhou, J.; Huang, B.; et al. Hierarchical quality-relevant feature representation for soft sensor modeling: a novel deep learning strategy. IEEE. Trans. Ind. Informat. 2020, 16, 3721-30.

26. Reigosa, D.; Briz, F.; Charro, C. B.; Garcia, P.; Guerrero, J. M. Active islanding detection using high-frequency signal injection. IEEE. Trans. Ind. Appl. 2012, 48, 1588-97.

27. Zhang, X.; Deng, X.; Cao, Y.; Xiao, L. Nonlinear predictable feature learning with explanatory reasoning for complicated industrial system fault diagnosis. Knowl. Based. Syst. 2024, 286, 111404.

28. Wang, L.; Mao, M.; Xie, J.; Liao, Z.; Zhang, H.; Li, H. Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model. Energy 2023, 262, 125592.

29. Fazi, M. B. Beyond human: deep learning, explainability and representation. Theory. Cult. Soc. 2021, 38, 55-77.

30. Hosain, M. T.; Jim, J. R.; Mridha, M. F.; Kabir, M. M. Explainable AI approaches in deep learning: advancements, applications and challenges. Comput. Electr. Eng. 2024, 117, 109246.

31. Jang, K.; Pilario, K. E. S.; Lee, N.; Moon, I.; Na, J. Explainable artificial intelligence for fault diagnosis of industrial processes. IEEE. Trans. Ind. Informat. 2023, 1-8.

32. Kidambi Raju, S.; Ramaswamy, S.; Eid, M. M.; et al. Enhanced dual convolutional neural network model using explainable artificial intelligence of fault prioritization for industrial 4.0. Sensors 2023, 23, 7011.

33. Ferraro, A.; Galli, A.; Moscato, V.; Sperlì, G. Evaluating eXplainable artificial intelligence tools for hard disk drive predictive maintenance. Artif. Intell. Rev. 2023, 56, 7279-314.

34. Wang, T.; Zheng, X.; Zhang, L.; Cui, Z.; Xu, C. A graph-based interpretability method for deep neural networks. Neurocomputing 2023, 555, 126651.

35. Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 2018, 120, 145301.

36. Boudraa, A. O.; Cexus, J. C. EMD-based signal filtering. IEEE. Trans. Instrum. Meas. 2007, 56, 2196-202.

37. Kopsinis, Y.; McLaughlin, S. Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE. Trans. Signal. Process. 2009, 57, 1351-62.

38. Xiong, Z.; Yao, J.; Huang, Y.; Yu, Z.; Liu, Y. A wind speed forecasting method based on EMD-MGM with switching QR loss function and novel subsequence superposition. Appl. Energy. 2024, 353, 122248.

39. Zhang, S.; Li, X.; Zong, M.; Zhu, X.; Wang, R. Efficient kNN classification with different numbers of nearest neighbors. IEEE. Trans. Neural. Netw. Learn. Syst. 2017, 29, 1774-85.

40. Kipf TN, Welling M. Semi-supervised slassification with graph convolutional networks. arXiv 2016. arXiv: 1609.02907. Available online: https://arxiv.org/abs/1609.02907. (accessed 11 Jan 2025).

41. Lv, M.; Li, Y.; Liang, H.; et al. A spatial–temporal variational graph attention autoencoder using interactive information for fault Detection in complex industrial processes. IEEE. Trans. Neural. Netw. Learn. Syst. 2024, 35, 3062-76.

Intelligence & Robotics
ISSN 2770-3541 (Online)
Follow Us

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/