Intelligent Machine Learning-Based Spectroscopy for Condition Monitoring of Energy Infrastructure: A Review Focused on Transformer Oils and Hydrogen Systems

With the advancement of industrial systems toward greater complexity and higher asset value, unexpected equipment failures now risk severe production interruptions, substantial economic costs, and critical safety hazards. Conventional maintenance strategies, which are primarily reactive or schedule-based, have proven inadequate in preventing unplanned downtime, underscoring a pressing demand for more intelligent monitoring solutions. In this context, intelligent spectral detection has arisen as a transformative methodology to bridge this gap. This review explores the integration of spectroscopic techniques with machine learning for equipment defect diagnosis and prognosis, with a particular focus on applications such as hydrogen leak detection and transformer oil aging assessment. Key aging indicators derived from spectral data are systematically evaluated to establish a robust basis for condition monitoring. The paper also identifies prevailing challenges in the field, including spectral data scarcity, limited model interpretability, and poor generalization across different operational scenarios. Future research directions emphasize the construction of large-scale, annotated spectral databases, the development of multimodal data fusion frameworks, and the optimization of lightweight algorithms for practical, real-time deployment. Ultimately, this work aims to provide a clear roadmap for implementing predictive maintenance paradigms, thereby contributing to safer, more reliable, and more efficient industrial operations.