蠕变呻吟噪声的主客观分类

J. Prezelj, Severin Huemer-Kals, K. Häsler
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引用次数: 0

摘要

粘滑效应发生在刹车片和制动盘之间的接触面。蠕变呻吟现象与高达500hz的频率范围内的粘滑效应有关。它对整个制动系统产生振动,并影响车辆的结构振动。振动传递到大的表面,产生可听的噪音。噪音令人不愉快,可以被认为是一种缺陷,尤其是对豪华车的司机来说。由蠕变呻吟噪声引起的刺耳和烦恼取决于许多因素。由于这个原因,车辆测试通常包括对蠕变呻吟噪声的主观分析。不幸的是,主观测试受到主观评价固有的不确定性的影响。为了减少这些不确定性,提高蠕变呻吟的评价水平,有必要建立蠕变呻吟噪声强度评价模型。为此目的,进行了1000多次测量的实验测试。从测量数据中提取并分析了许多不同的特征。选取准声发射、模拟声压级和振动级特征,利用Kohonen自组织图和K-Means算法将蠕变呻吟分为8类。两种无监督分类算法的结果都与主观量化相关,范围从1(非常差)到10(优秀)。通过K-Means算法对无监督算法分类进行正确解释后,得到了算法分类与蠕变呻吟严酷度主观量化之间的相关性。结果表明,Kohonen的自组织图与振动声信号的选定特征相结合,提供了易于使用的结果,无需任何主观评价。
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Objective and Subjective Classification of Creep Groan Noise
Stick-Slip effects occur at the contact surface between the brake pads and the brake disc. The Creep Groan phenomenon is associated with stick-slip effects in the frequency range up to 500 Hz. It induces vibrations on the entire brake system and influences the structural vibrations of the vehicle. Vibrations are transmitted to large surfaces which produce audible noise. Creep Groan Noise is unpleasant and can be considered as a defect, especially for drivers of luxury cars. Harshness and annoyance caused by Creep Groan noise depends on many factors. For this reason, vehicle tests often include a subjective analysis of the Creep Groan Noise. Unfortunately, subjective tests suffer from the inherent uncertainties of subjective evaluation. A model for the assessment of Creep Groan noise harshness is necessary to reduce these uncertainties and to improve the Creep Groan assessment. For this purpose, experimental tests with over 1000 measurements were carried out. Many different features from the measurement data were extracted and analyzed. Selected features (quasi acoustic emission, simulated sound pressure level and vibration level) were used to classify Creep Groan into eight annoyance classes using Kohonen's Self Organizing Maps and a K-Means algorithm. The results of both unsupervised classification algorithms were correlated with a subjective quantification ranging from 1 (very poor) to 10 (excellent). After the right interpretation of the unsupervised algorithmic classification, performed with the K-Means algorithm, the correlation between the algorithmic classification and the subjective quantification of the creep groan harshness is obtained. It is shown that Kohonen's self-organizing map, in combination with selected features of vibroacoustic signals, provides readily usable results without any need for subjective evaluations.
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