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A study published in Energy Storage and Saving by researchers from Xi'an Jiaotong University introduces a novel approach to FDD in HVAC systems. This research leverages a modified transformer model and adapter-based transfer learning to enhance the generalizability of FDD models across various HVAC systems.

The team developed a transformer model enhanced with an encoder and two decoders, enabling simultaneous identification of multiple fault types and severities. This innovation is complemented by an adapter-based transfer learning strategy, allowing the model to adapt efficiently across various HVAC systems, even with limited data.

Two designed transfer learning scenarios demonstrate the effectiveness of the proposed HVAC FDD transfer learning framework, compared with the popular fine-tuning method.