基于改进的Zwicker算法评价动车组车内异响

Evaluation of Abnormal Noise in EMU by Modified Zwicker Algorithm

声品质评价参数包括响度、尖锐度和粗糙度。以响度为例对车内声品质的瞬态特性——异响进行评价,发现Zwicker算法不能准确评价异响特性参数N10相同的异响,因此对Zwicker算法进行改进,提出MZSPA算法。以时域信号中排名前10%的噪声幅值平均值M10作为异响特性参数,对350km·h-1车速的CRH380BL型动车组的车内异响进行评价,结果表明:相比Zwicker算法,采用MZSPA算法评价车内异响时,捕捉到了后端板件随机振动引起的明显异响,与人耳听觉系统的感知一致,验证了MZSPA算法的可行性。

The evaluation parameters for sound quality include loudness, sharpness and roughness. With loudness for instance, the transient attribute of the interior sound quality, known as abnormal noise, was evaluated using the above parameters. It was found that the Zwieker algorithm could not accurately evaluate the abnormal noise attributes with the same N10 value. Thus, MZSPA algorithm was proposed to optimize the Zwicker algorithm. The average value M10 from the top 10% noise amplitudes of a signal in time domain was used as the abnormal noise attribute parameter to evaluate the interior abnormal noise of the CRH380BL EMU at 350 km ·h-1. The results show that the random vibration induced abnormal noise of a thin-wall plate at the rear end is captured by the MZSPA algorithm compared with the Zwicker algo- rithm, which presents high agreement with the hearing sense of testers and validates the feasibility of the MZSPA algorithm.