Channel-Attentive Temporal Convolutional Networks (CA-TCN) for Interpretable Fault Diagnosis in the Tennessee Eastman Process
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Published
Oct 20, 2026
Pratyush Kumar Pal
Sumona Ray
Narottam Behera
Somnath Chowdhury
Abhiram Hens
Sandip Kumar Lahiri
Abstract
Fault detection and diagnosis (FDD), Fault classification in the Tennessee Eastman Process (TEP) involves strongly coupled, nonlinear multivariate trajectories. The present research evaluates a residual Temporal Convolutional Network (TCN) with Squeeze-and-Excitation (SE) channel attention, denoted CA-TCN. We compare CA-TCN with plain TCN, Long Short-Term Memory (LSTM), and Convolutional Neural Network-LSTM (CNN-LSTM) baselines. CA-TCN and TCN+SE refer to the same implemented architecture.
In the primary five-seed comparison, plain TCN achieved a mean accuracy of 99.8756 ± 0.0807%, compared with 99.8511 ± 0.1104% for CA-TCN, 99.1711 ± 0.9467% for LSTM, and 99.8444 ± 0.0820% for CNN-LSTM. A separate matched five-seed TCN/CA-TCN ablation yielded mean accuracies of 99.8000 ± 0.1199% and 99.7756 ± 0.1823%, respectively.
The mean paired CA-TCN-minus-TCN difference was −0.0244 percentage points. The exploratory paired analysis did not identify a statistically detectable difference in accuracy. Adding the SE module introduced 4,192 additional trainable parameters and increased the measured training time while providing explicit reweighting of 64 learned latent feature channels.
These coefficients are interpreted as internal latent-channel recalibration rather than sensor-importance scores. The results therefore characterize the performance–complexity trade-off associated with adding SE attention to the residual TCN backbone.
The conclusions are limited to offline whole-trajectory classification on the evaluated TEP benchmark; cross-domain generalization and online fault-detection performance were not assessed.
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Keywords
Fault Detection and Diagnosis; Temporal Convolutional Network; Channel Attention; Tennessee Eastman Process; Deep Learning; Process Monitoring
References
Agarwal, P., Gonzalez, J. I. M., Elkamel, A., & Budman, H. (2022). Hierarchical Deep LSTM for Fault Detection and Diagnosis for a Chemical Process. Processes, 10(12). https://doi.org/10.3390/pr10122557
Agarwal, P., Tamer, M., & Budman, H. (2020). Assessing observability using supervised autoencoders with an application to the Tennessee Eastman process. IFAC-PapersOnLine, 53(2), 206–211. https://doi.org/10.1016/j.ifacol.2020.12.122
Agarwal, P., Tamer, M., & Budman, H. (2021). Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes. https://doi.org/10.1016/j.compchemeng.2021.107467
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (arXiv:1803.01271). arXiv. https://doi.org/10.48550/arXiv.1803.01271
Chadha, G. S., & Schwung, A. (2017). Comparison of deep neural network architectures for fault detection in Tennessee Eastman process. 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1–8. https://doi.org/10.1109/ETFA.2017.8247619
Chen, B., Zhang, H., He, F., Zhang, C., Chen, Y., Liao, H., & Zheng, S. (2023). Modified Q-sigma Rule for Incipient Fault Detection in Industrial Processes on Analysis of Intermittent Process Variation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4375935
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001). Fault Detection and Diagnosis in Industrial Systems. Springer London. https://doi.org/10.1007/978-1-4471-0347-9
Downs, J.J., Vogel, E.F., 1993 A plant-wide industrial
process control problem Comput. Chem. Eng. 17
(3), 245–255 https://doi.org/10.1016/0098-
1354(93)80018-I
Han, Y., Ding, N., Geng, Z., Wang, Z., & Chu, C. (2020). An optimized long short-term memory network based fault diagnosis model for chemical processes. Journal of Process Control, 92, 161–168. https://doi.org/10.1016/j.jprocont.2020.06.005
Heo, S., & Lee, J. H. (2018). Fault detection and classification using artificial neural networks. IFAC-PapersOnLine, 51(18), 470–475. https://doi.org/10.1016/j.ifacol.2018.09.380
Heo, S., & Lee, J. H. (2019). Statistical process monitoring of the Tennessee Eastman process using parallel autoassociative neural networks and a large dataset. Processes, 7(7). https://doi.org/10.3390/pr7070411
Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Computation 9. https://doi.org/10.1162/neco.1997.9.8.1735
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-Excitation Networks. https://doi.org/10.48550/arXiv.1709.01507
Labbaf-Khaniki, M. A., Manthouri, M., & Ajami, H. (2024). Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process.
https://doi.org/10.48550/arXiv.2403.10842
Lea, C., Vidal, R., Reiter, A., & Hager, G. D. (2016). Temporal Convolutional Networks: A Unified Approach to Action Segmentation (arXiv:1608.08242). arXiv. https://doi.org/10.48550/arXiv.1608.08242
Li, M., & Shao, Y. (2021). Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes. http://arxiv.org/abs/2101.06993
Lomov, I., Lyubimov, M., Makarov, I., & Zhukov, L. E. (2021). Fault detection in Tennessee Eastman process with temporal deep learning models. Journal of Industrial Information Integration, 23, 100216. https://doi.org/10.1016/j.jii.2021.100216
Onel, M., Kieslich, C. A., & Pistikopoulos, E. N. (2019). A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process. AIChE Journal, 65(3), 992–1005. https://doi.org/10.1002/aic.16497
Pal, P. K., Ray, S., Behera, N., Chowdhury, S., Hens, A., & Lahiri, S. K. (2026). Compact CNN-Transformer Model for Robust Fault Diagnosis in Large-Scale Chemical Processes. International Journal of Prognostics and Health Management, 17(1). https://doi.org/10.36001/ijphm.2026.v17i1.4744
Reinartz, C., Kulahci, M., & Ravn, O. (2021). An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems. Computers and Chemical Engineering, 149. https://doi.org/10.1016/j.compchemeng.2021.107281
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All you Need.
Verma, R., Yerolla, R., & Besta, C. S. (2022). Deep Learning-based Fault Detection in the Tennessee Eastman Process. Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 228–233. https://doi.org/10.1109/ICAIS53314.2022.9743021
Wang, J., Li, Y., Gao, R. X., & Zhang, F. (2022). Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability. Journal of Manufacturing Systems, 63, 381–391. https://doi.org/10.1016/j.jmsy.2022.04.004
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 (Vol. 11211, pp. 3–19). Springer International Publishing. https://doi.org/10.1007/978-3-030-01234-2_1
Yin, S., Ding, S. X., Haghani, A., Hao, H., & Zhang, P. (2012). A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 22(9), 1567–1581. https://doi.org/10.1016/j.jprocont.2012.06.009
Yin, S., Gao, X., Karimi, H. R., & Zhu, X. (2014). Study on support vector machine-based fault detection in Tennessee Eastman process. Abstract and Applied Analysis, 2014. https://doi.org/10.1155/2014/836895
Zhang, C., Guo, Q., & Li, Y. (2020). Fault detection in the Tennessee eastman benchmark process using principal component difference based on k-nearest neighbors. IEEE Access, 8, 49999–50009. https://doi.org/10.1109/ACCESS.2020.2977421
Zhang, C., Zheng, X., & Li, Y. (2021). A novel fault detection and diagnosis scheme based on independent component analysis-statistical characteristics: Application on the tennessee eastman benchmark process. Journal of Chemical Engineering of Japan, 54(6), 304–312. https://doi.org/10.1252/jcej.20we045
Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017). A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access, 5, 26901–26911.
Zhang, L., Song, Z., Zhang, Q., & Peng, Z. (2022). Generalized transformer in fault diagnosis of Tennessee Eastman process. Neural Computing and Applications, 34(11), 8575–8585. https://doi.org/10.1007/s00521-021-06711-2
Zhao, S., Duan, Y., Roy, N., & Zhang, B. (2025). A novel fault diagnosis framework empowered by LSTM and attention: A case study on the Tennessee Eastman process. The Canadian Journal of Chemical Engineering, 103(4), 1763–1785. https://doi.org/10.1002/cjce.25460
Agarwal, P., Tamer, M., & Budman, H. (2020). Assessing observability using supervised autoencoders with an application to the Tennessee Eastman process. IFAC-PapersOnLine, 53(2), 206–211. https://doi.org/10.1016/j.ifacol.2020.12.122
Agarwal, P., Tamer, M., & Budman, H. (2021). Explainability: Relevance based Dynamic Deep Learning Algorithm for Fault Detection and Diagnosis in Chemical Processes. https://doi.org/10.1016/j.compchemeng.2021.107467
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling (arXiv:1803.01271). arXiv. https://doi.org/10.48550/arXiv.1803.01271
Chadha, G. S., & Schwung, A. (2017). Comparison of deep neural network architectures for fault detection in Tennessee Eastman process. 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1–8. https://doi.org/10.1109/ETFA.2017.8247619
Chen, B., Zhang, H., He, F., Zhang, C., Chen, Y., Liao, H., & Zheng, S. (2023). Modified Q-sigma Rule for Incipient Fault Detection in Industrial Processes on Analysis of Intermittent Process Variation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4375935
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001). Fault Detection and Diagnosis in Industrial Systems. Springer London. https://doi.org/10.1007/978-1-4471-0347-9
Downs, J.J., Vogel, E.F., 1993 A plant-wide industrial
process control problem Comput. Chem. Eng. 17
(3), 245–255 https://doi.org/10.1016/0098-
1354(93)80018-I
Han, Y., Ding, N., Geng, Z., Wang, Z., & Chu, C. (2020). An optimized long short-term memory network based fault diagnosis model for chemical processes. Journal of Process Control, 92, 161–168. https://doi.org/10.1016/j.jprocont.2020.06.005
Heo, S., & Lee, J. H. (2018). Fault detection and classification using artificial neural networks. IFAC-PapersOnLine, 51(18), 470–475. https://doi.org/10.1016/j.ifacol.2018.09.380
Heo, S., & Lee, J. H. (2019). Statistical process monitoring of the Tennessee Eastman process using parallel autoassociative neural networks and a large dataset. Processes, 7(7). https://doi.org/10.3390/pr7070411
Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Computation 9. https://doi.org/10.1162/neco.1997.9.8.1735
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-Excitation Networks. https://doi.org/10.48550/arXiv.1709.01507
Labbaf-Khaniki, M. A., Manthouri, M., & Ajami, H. (2024). Twin Transformer using Gated Dynamic Learnable Attention mechanism for Fault Detection and Diagnosis in the Tennessee Eastman Process.
https://doi.org/10.48550/arXiv.2403.10842
Lea, C., Vidal, R., Reiter, A., & Hager, G. D. (2016). Temporal Convolutional Networks: A Unified Approach to Action Segmentation (arXiv:1608.08242). arXiv. https://doi.org/10.48550/arXiv.1608.08242
Li, M., & Shao, Y. (2021). Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes. http://arxiv.org/abs/2101.06993
Lomov, I., Lyubimov, M., Makarov, I., & Zhukov, L. E. (2021). Fault detection in Tennessee Eastman process with temporal deep learning models. Journal of Industrial Information Integration, 23, 100216. https://doi.org/10.1016/j.jii.2021.100216
Onel, M., Kieslich, C. A., & Pistikopoulos, E. N. (2019). A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process. AIChE Journal, 65(3), 992–1005. https://doi.org/10.1002/aic.16497
Pal, P. K., Ray, S., Behera, N., Chowdhury, S., Hens, A., & Lahiri, S. K. (2026). Compact CNN-Transformer Model for Robust Fault Diagnosis in Large-Scale Chemical Processes. International Journal of Prognostics and Health Management, 17(1). https://doi.org/10.36001/ijphm.2026.v17i1.4744
Reinartz, C., Kulahci, M., & Ravn, O. (2021). An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems. Computers and Chemical Engineering, 149. https://doi.org/10.1016/j.compchemeng.2021.107281
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All you Need.
Verma, R., Yerolla, R., & Besta, C. S. (2022). Deep Learning-based Fault Detection in the Tennessee Eastman Process. Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 228–233. https://doi.org/10.1109/ICAIS53314.2022.9743021
Wang, J., Li, Y., Gao, R. X., & Zhang, F. (2022). Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability. Journal of Manufacturing Systems, 63, 381–391. https://doi.org/10.1016/j.jmsy.2022.04.004
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). CBAM: Convolutional Block Attention Module. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 (Vol. 11211, pp. 3–19). Springer International Publishing. https://doi.org/10.1007/978-3-030-01234-2_1
Yin, S., Ding, S. X., Haghani, A., Hao, H., & Zhang, P. (2012). A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control, 22(9), 1567–1581. https://doi.org/10.1016/j.jprocont.2012.06.009
Yin, S., Gao, X., Karimi, H. R., & Zhu, X. (2014). Study on support vector machine-based fault detection in Tennessee Eastman process. Abstract and Applied Analysis, 2014. https://doi.org/10.1155/2014/836895
Zhang, C., Guo, Q., & Li, Y. (2020). Fault detection in the Tennessee eastman benchmark process using principal component difference based on k-nearest neighbors. IEEE Access, 8, 49999–50009. https://doi.org/10.1109/ACCESS.2020.2977421
Zhang, C., Zheng, X., & Li, Y. (2021). A novel fault detection and diagnosis scheme based on independent component analysis-statistical characteristics: Application on the tennessee eastman benchmark process. Journal of Chemical Engineering of Japan, 54(6), 304–312. https://doi.org/10.1252/jcej.20we045
Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017). A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access, 5, 26901–26911.
Zhang, L., Song, Z., Zhang, Q., & Peng, Z. (2022). Generalized transformer in fault diagnosis of Tennessee Eastman process. Neural Computing and Applications, 34(11), 8575–8585. https://doi.org/10.1007/s00521-021-06711-2
Zhao, S., Duan, Y., Roy, N., & Zhang, B. (2025). A novel fault diagnosis framework empowered by LSTM and attention: A case study on the Tennessee Eastman process. The Canadian Journal of Chemical Engineering, 103(4), 1763–1785. https://doi.org/10.1002/cjce.25460
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