Physically-consistent-WGAN based small sample fault diagnosis for industrial processes

doi:10.1016/j.cjche.2024.10.028

Abstract

Abstract: In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversarial networks for smallsample data has achieved a wide range of applications. However, the current generative adversarial networks applied in industrial processes do not impose realistic physical constraints on the generation of data, resulting in the generation of data that do not have realistic physical consistency. To address this problem, this paper proposes a physical consistency-based WGAN, designs a loss function containing physical constraints for industrial processes, and validates the effectiveness of the method using a common dataset in the field of industrial process fault diagnosis. The experimental results show that the proposed method not only makes the generated data consistent with the physical constraints of the industrial process, but also has better fault diagnosis performance than the existing GAN-based methods.

Key words: Chemical processes, Fault diagnosis, Physical consistency, Generative adversarial networks, Small sample data

摘要： In real industrial scenarios, equipment cannot be operated in a faulty state for a long time, resulting in a very limited number of available fault samples, and the method of data augmentation using generative adversarial networks for smallsample data has achieved a wide range of applications. However, the current generative adversarial networks applied in industrial processes do not impose realistic physical constraints on the generation of data, resulting in the generation of data that do not have realistic physical consistency. To address this problem, this paper proposes a physical consistency-based WGAN, designs a loss function containing physical constraints for industrial processes, and validates the effectiveness of the method using a common dataset in the field of industrial process fault diagnosis. The experimental results show that the proposed method not only makes the generated data consistent with the physical constraints of the industrial process, but also has better fault diagnosis performance than the existing GAN-based methods.

关键词: Chemical processes, Fault diagnosis, Physical consistency, Generative adversarial networks, Small sample data

Siyu Tang, Hongbo Shi, Bing Song, Yang Tao, Shuai Tan. Physically-consistent-WGAN based small sample fault diagnosis for industrial processes[J]. Chinese Journal of Chemical Engineering, 2025, 78(2): 163-174.

Siyu Tang, Hongbo Shi, Bing Song, Yang Tao, Shuai Tan. Physically-consistent-WGAN based small sample fault diagnosis for industrial processes[J]. 中国化学工程学报, 2025, 78(2): 163-174.

References

[1] J.H. Zheng, H.J. Wang, Z.H. Song, Z.Q. Ge, Ensemble semi-supervised Fisher discriminant analysis model for fault classification in industrial processes, ISA Trans. 92 (2019) 109-117.
[2] Z.L. Ren, Y.C. Jiang, X.B. Yang, Y.Q. Tang, W.S. Zhang, Learnable faster kernel-PCA for nonlinear fault detection: deep autoencoder-based realization, J. Ind. Inf. Integr. 40 (2024) 100622.
[3] A. Fazli, J. Poshtan, Wind turbine fault detection and isolation robust against data imbalance using KNN, Energy Sci. Eng. 12 (3) (2024) 1174-1186.
[4] X.F. Yuan, W.W. Xu, Y.L. Wang, C.H. Yang, W.H. Gui, A deep residual PLS for data-driven quality prediction modeling in industrial process, IEEE/CAA J. Autom. Sin. 11 (8) (2024) 1777-1785.
[5] W.D. Tian, Z.J. Liu, L.N. Li, S.F. Zhang, C.K. Li, Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning, Chin. J. Chem. Eng. 28 (7) (2020) 1875-1883.
[6] H. Wu, J.S. Zhao, Deep convolutional neural network model based chemical process fault diagnosis, Comput. Chem. Eng. 115 (2018) 185-197.
[7] C. Guo, W.K. Hu, F. Yang, D.X. Huang, Deep learning technique for process fault detection and diagnosis in the presence of incomplete data, Chin. J. Chem. Eng. 28 (9) (2020) 2358-2367.
[8] C.T. Wang, H.B. Shi, B. Song, Y. Tao, Hierarchical multihead self-attention for time-series-based fault diagnosis, Chin. J. Chem. Eng. 70 (2024) 104-117.
[9] L.J. Feng, C.H. Zhao, Fault description based attribute transfer for zero-sample industrial fault diagnosis, IEEE Trans. Ind. Inf. 17 (3) (2021) 1852-1862.
[10] Z.J. Yao, P.Y. Song, C.H. Zhao, Finding trustworthy neighbors: graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity, J. Process Control 129 (2023) 103038.
[11] S.W. Xiong, L. Zhou, Y.Y. Dai, X. Ji, Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis, Chin. J. Chem. Eng. 56 (2023) 1-14.
[12] P. Peng, Y. Wang, W.J. Zhang, Y. Zhang, H.M. Zhang, Imbalanced process fault diagnosis using enhanced auxiliary classifier GAN, In:2020 Chinese Automation Congress (CAC), Shanghai, China. IEEE, 2020.
[13] R.S. Qin, J.S. Zhao, High-efficiency generative adversarial network model for chemical process fault diagnosis, IFAC-PapersOnLine 55 (7) (2022) 732-737.
[14] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley,S. Ozair,A. Courville, Y. Bengio, Generative adversarial nets, In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2014.
[15] X.W. Jia, J. Willard, A. Karpatne, J.S. Read, J.A. Zwart, M. Steinbach, V. Kumar, Physics-guided machine learning for scientific discovery: an application in simulating lake temperature profiles, ACM/IMS Trans. Data Sci. 2 (3) (2021) 1-26.
[16] M. Raissi, P. Perdikaris, G.E. Karniadakis, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378 (2019) 686-707.
[17] K. Yonekura, Physics-guided generative adversarial network to learn physical models, (2023): 2304.11488.
[18] G. Achour, W.J. Sung, O.J. Pinon-Fischer, D.N. Mavris, Development of a conditional generative adversarial network for airfoil shape optimization, In: AIAA Scitech 2020 Forum. Orlando, FL. Reston, Virginia, 2020.
[19] G. Manca, Tennessee-Eastman-Process alarm management dataset, IEEE DataPort, 2020, doi: 10.21227/326k-qr90.
[20] S.S. Ge, C.C. Hang, T. Zhang, Nonlinear adaptive control using neural networks and its application to CSTR systems, J. Process Control 9 (4) (1999) 313-323.