Liuzhou Vocational and Technical College, Liuzhou 545006, China.
National Laboratory for Rail Transportation, Southwest Jiaotong University, Chengdu 610031, China.
Comput Intell Neurosci. 2022 Mar 31;2022:3873651. doi: 10.1155/2022/3873651. eCollection 2022.
Wiper motor noise has an important impact on vehicle comfort. Accurate prediction of wiper motor noise can obtain motor NVH performance in motor manufacturing or earlier stage and provide necessary support for NVH performance design of parts and vehicles. However, the prediction accuracy of wiper motor noise by the traditional CAE or testing method is low. Data-driven technology provides a new idea for wiper motor noise prediction with its advantages of high efficiency and high precision. This paper studies the wiper motor noise prediction algorithm based on the motor vibration signal, respectively, using the transmission path analysis theory and the support vector machine theory, and carries on the test verification and comparative analysis of the effect. The results show that the method based on support vector machine is more accurate in the prediction of wiper motor noise and has higher practical engineering value.
雨刮电机噪声对车辆舒适性有重要影响。准确预测雨刮电机噪声，可以在电机制造或更早阶段获得电机 NVH 性能，并为零部件和整车的 NVH 性能设计提供必要的支持。然而，传统的 CAE 或测试方法对雨刮电机噪声的预测精度较低。数据驱动技术以其高效、高精度的优势，为雨刮电机噪声预测提供了新的思路。本文分别基于电机振动信号，利用传递路径分析理论和支持向量机理论，研究了雨刮电机噪声预测算法，并对效果进行了试验验证和对比分析。结果表明，基于支持向量机的方法在雨刮电机噪声预测方面更为准确，具有更高的实际工程价值。
