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| Separation and identification of noise source at wheel loader's cab |
| LIU Hong-jun， BI Feng-rong， ZHANG Jian |
| College of Mechanical Engineering， Tianjin University， Tianjin 300072， China |
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Abstract In order to ameliorate the noise condition of the wheel loader, optimize the level of noise analysis technology and improve product competitiveness, a study on identifying the noise source from the cab of the ZL50 wheel loader based on the robust independent component analysis (RobustICA) was carried out. The RobustICA was adopted to separate noise signals of the cab and a series of independent components were obtained. Wavelet transform and coherence analysis were used to analyze these independent components. From the results of time-frequency analysis and correlation analysis, the corresponding relationship was determined. Results showed that these independent components corresponded to exhaust noise and fan noise of the loader which constituted the ingredients of the noise near driver’s ear. The superiority of RobustICA is verified using in the field of sound source separation and identification.
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Received: 11 September 2014
Published: 28 February 2015
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轮式装载机驾驶室噪声源分离和识别
为了改善装载机的噪声状况，提高产品的噪声分析技术水平及产品市场竞争力，采用鲁棒独立分量分析（RobustICA）的方法，对ZL50轮式装载机驾驶室噪声进行了声源识别的研究．主要采用RobustICA算法对测得的驾驶室噪声信号进行了盲源分离，得到一系列独立的噪声分量，利用连续小波变换和相干分析对分离得到的各独立分量进行了分析，时频分析和相干分析结果确定了分离得到的各独立分量和不同噪声源的对应关系．结果表明，采用该方法分离得到的独立分量分别对应着装载机的排气噪声和风扇噪声，且两者为司机耳旁噪声的主要成分．验证了鲁棒独立分量分析在声源分离和识别领域的优越性．
关键词：
装载机,
驾驶室,
噪声,
鲁棒独立分量分析,
小波变换
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