用机器学习快速预估发电机噪声，助力早期投标决策
A fast sound power prediction tool for genset noise using machine learning
- 采用核岭回归等算法，基于设计参数预测未建发电机噪声
- 核岭回归平均误差仅5 dBA，可精准估计声功率级
- 适合营销、销售团队在无实测数据时做早期噪声评估
本文研究了核岭回归（KRR）、Huber回归（HR）和高斯过程回归（GPR）三种机器学习回归算法在发电机组（genset）声功率级预测中的应用，为市场与销售团队在早期投标阶段提供重要价值。当发动机尺寸和机组隔间尺寸尚未确定，且缺乏实测噪声数据时，这些算法可对未建造的发电机进行可靠噪声水平估算。研究基于康明斯声学技术中心（ATC）在半消声室中开展的100余次高保真度实验数据，符合ISO 3744标准。利用投标及初始设计阶段可获取的信息，KRR模型实现平均精度在5 dBA以内；HR与GPR虽略高，但均能有效捕捉不同发电机配置下的整体噪声趋势。该方法为发电机设计早期噪声预测提供了可行路径。
原文摘要 · Abstract (English)
This paper investigates the application of machine learning regression algorithms Kernel Ridge Regression (KRR), Huber Regressor (HR), and Gaussian Process Regression (GPR) for predicting sound power levels of gensets, offering significant value for marketing and sales teams during the early bidding process. When engine sizes and genset enclosure dimensions are tentative, and measured noise data is unavailable, these algorithms enable reliable noise level estimation for unbuilt gensets. The study utilizes high fidelity datasets from over 100 experiments conducted at Cummins Acoustics Technology Center (ATC) in a hemi-anechoic chamber, adhering to ISO 3744 standards. By using readily available information from the bidding and initial design stages, KRR predicts sound power with an average accuracy of within 5 dBA. While HR and GPR show slightly higher prediction errors, all models effectively capture the overall noise trends across various genset configurations. These findings present a promising method for early-stage noise estimation in genset design.
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