用迁移学习精准预测空调压缩机噪声，提升环境变化下的预测能力。
Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals
- 基于域不变偏最小二乘法，从振动与热力学信号中提取通用特征。
- 振动信号模型误差始终低于3 dB，显著优于传统方法。
- 揭示结构振动比热力状态更直接关联噪声生成，适合设备故障预警场景。
双转子压缩机产生的二阶谐波（2f）是变制冷剂流量（VRF）室外机的主要低频噪声源，但其幅值随环境热负荷和阀开度剧烈波动，传统机理模型难以准确评估。本文提出一种基于域不变偏最小二乘法（Di-PLS）的无监督迁移学习方法，利用热力学信号与加速度信号分别构建预测模型，并系统对比了其与传统偏最小二乘法（PLS）的泛化性能。结果表明，Di-PLS通过提取跨条件共性特征并最小化源域与目标域分布差异，显著优于PLS。特别是基于加速度信号的Di-PLS模型在所有测试案例中均保持预测误差小于3 dB。该优势凸显了：尽管热力状态驱动动态变化，但结构振动与声辐射具有更强且更直接的因果关联。
原文摘要 · Abstract (English)
The second-order harmonic (2f) component generated by twin-rotary compressor is a dominant low-frequency noise source of variable refrigerant flow (VRF) outdoor units, yet its amplitude fluctuates strongly with environmental thermal load and valve opening, making it difficult to assess accurately using conventional mechanism-based models. This paper proposes an unsupervised transfer learning method based on Domain-invariant Partial Least Squares (Di-PLS) to accurately predict 2f noise levels under new conditions using different signals. Prediction models utilizing thermodynamic signals and acceleration signals are constructed respectively, and the generalization performance of the proposed Di-PLS is systematically compared with traditional Partial Least Squares (PLS). Results demonstrate that Di-PLS significantly outperforms PLS by extracting cross-condition common features and minimizing the distribution discrepancy between the source and target domains. Specifically, the acceleration-based Di-PLS model achieves the best performance, maintaining prediction errors within 3 dB for all test cases. This superiority over thermodynamic-based models highlights a physical insight: while thermodynamic states drive dynamic changes, structural vibration possesses a stronger and more direct causal link to acoustic radiation.
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