Deep Learning-Based Fault Detection and Diagnosis for Complex Engineering Systems (International Journal of Artificial Intelligence Engineering and Systems, 2026)
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.
Keywords: Deep learning, fault detection, fault diagnosis, complex engineering systems, system architecture, deployment infrastructure, robustness, governance, socio-technical systems