Deep Learning-Based Fault Detection and Diagnosis for Complex Engineering Systems
Keywords:
Deep learning, fault detection, fault diagnosis, complex engineering systems, system architecture, deployment infrastructure, robustness, governance, socio-technical systemsAbstract
Fault detection and diagnosis in complex engineering systems constitutes a persistent challenge at the intersection of reliability engineering, artificial intelligence, and large-scale infrastructure management. Deep learning has emerged as a powerful paradigm for extracting fault signatures from high-dimensional, heterogeneous sensor data, yet its integration into operational environments demands careful consideration of system-level trade-offs, architectural constraints, and socio-technical governance. This paper provides a comprehensive interdisciplinary examination of deep learning-based fault detection and diagnosis, moving beyond algorithmic performance metrics to interrogate the structural, infrastructural, and regulatory dimensions that shape real-world viability. The discussion first situates deep learning architectures within the broader landscape of condition monitoring, highlighting how convolutional, recurrent, and graph-based models reconfigure the traditional diagnostic pipeline. It then analyzes critical system-level tensions involving accuracy, interpretability, computational complexity, and latency, emphasizing that diagnostic models are embedded in decision-making loops where explainability directly affects trust and safety certification. Deployment considerations are scrutinized through the lens of edge-cloud integration, digital twin synchronization, and data pipeline resilience, revealing how infrastructure choices constrain model selection and update cadence. The paper further explores governance and fairness dimensions, addressing data privacy across organizational boundaries, algorithmic bias across operating regimes, and the regulatory vacuum surrounding learning-enabled diagnostic components. Robustness is examined through adversarial vulnerability, concept drift, and fault-tolerant design, while sustainability concerns related to energy consumption and lifecycle model maintenance are integrated into a holistic system perspective. The analysis culminates in the argument that deep learning-based fault detection and diagnosis must be treated as a socio-technical system design problem, where technical architectures co-evolve with institutional policies, workforce dynamics, and lifecycle sustainability criteria.
References
1. Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.
2. Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis Part I: Quantitative model-based methods. Computers & Chemical Engineering, 27(3), 293–311.
3. Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241–265.
4. Zhang, W., Peng, G., Li, C., Chen, Y., & Zhang, Z. (2017). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17(2), 425.
5. Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.
6. Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 72–73, 303–315.
7. Wen, L., Li, X., Gao, L., & Zhang, Y. (2018). A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7), 5990–5998.
8. Shao, H., Jiang, H., Lin, Y., & Li, X. (2018). A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems and Signal Processing, 102, 278–297.
9. Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148.
10. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.
11. Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
12. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
13. Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.
14. Yao, S., Zhao, Y., Zhang, A., Hu, S., Shao, H., & Abdelzaher, T. (2018). Deep learning for the internet of things. Computer, 51(5), 32–41.
15. ISO 26262-1:2018. Road vehicles — Functional safety. International Organization for Standardization.
16. Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Network, 32(1), 96–101.
17. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
18. Akhtar, N., & Mian, A. (2018). Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6, 14410–14430.
19. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.
20. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.
21. European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). COM(2021) 206 final.
22. Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261.
23. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
24. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
25. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... & Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Engineering and Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
