Deep Learning Techniques for Fault Diagnosis in Interconnected Systems: A Comprehensive Review and Future Directions

Abstract

As systems in industry become increasingly interconnected and sophisticated, the task of fault detection and diagnosis becomes significantly more difficult. Predictive maintenance, in conjunction with sophisticated multimodal learning methods, has been found to be an effective solution for tackling such challenges. Presently, data are collected across numerous sources, ranging from sensors and operational variables to environmental variables, making it vital to combine these heterogeneous data for effective diagnostics. Advanced learning methods like deep learning, transfer learning, and hybrid models are tailored to processing and aggregating such disparate streams of data, thereby leading to higher diagnostic accuracy. This leads to more efficient and reliable predictive maintenance methods. This paper provides a comprehensive review of how various learning methods are applied to fault diagnosis in interconnected systems, particularly in predictive maintenance. It examines different approaches that integrate data across domains, evaluating how each contributes to improved fault detection and enhanced system reliability. Additionally, it addresses emerging research areas, such as real-time fault detection, innovative data fusion processes, and the increasing application in power grids, manufacturing, and the automation sector. This paper serves as a valuable resource for both researchers and practitioners, emphasizing the significant potential of multimodal learning in advancing fault diagnosis and predictive maintenance within increasingly interconnected and complex systems.

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