Reconfigurable Streaming Architecture for AI-Based Fault Prediction of Power Equipment: Initial Analysis and Discussion

High voltage power equipment, such as transformers, presents highly complex structures, operational conditions, and mechanisms. Effective condition monitoring and fault prediction for these systems require the integration of vast multimodal datasets, including electrical, mechanical, thermal, and environmental signals. This complexity places strong demands on lightweight computation and high-performance edge computing devices capable of real-time processing. In this study, current popular computing architectures, including CPU, Nvidia GPGPU and Google TPU, are firstly reviewed. The strength and the drawbacks of these architectures are discussed considering the requirements from the application of AI-based fault detection in power equipment. In the end, reconfigurable streaming architecture is highlighted and suggested to overcome the limitations of current popular computing architectures.