Noise Source Identification Applied in Electric Power Industry Using Microphone Arrays

The noise source identification is an important issue in noise reduction and condition monitoring(CM) for machines in- site using microphone arrays. In this paper, we propose a new approach to optimize array configuration based on particles swarm optimization algorithm in order to improve noise source identification and condition monitoring performance. Two distinct optimized array configurations are designed under the certain conditions. Furthermore, an acoustic imaging equipment is developed to carry out experiments on transformer substation equipment and wind turbine generator, which demonstrate that the acoustic imaging system allows a high resolution in identifying main noise sources for noise reduction and abnormal noise sources for condition monitoring.

Investigation on Major Noise Sources of Vehicle Diesel Engine Based on Sound Intensity Measurement

In order to reduce the noise and vibration of the diesel engine,it is crucial to exactly identify the engine noise source character.Based on "two-microphone" method,the sound intensity measurement of a vehicle four-stroke diesel engine was carried out in a hemi-anechoic chamber.Then the sound intensity contour maps were obtained from the measurement results and the main noise components of different frequencies on all the measurement surfaces were picked out to construct contour maps.By analysizing the relationship between the characteristics of contour maps and the space distribution of the engine compartment,the major sources of the exterior radiation noise of the diesel engine were identified.The results provided a creditable basis for improving the noise performance of the engine in the next phase.

Sound Source Localisation for a High-Speed Train and Its Transfer Path to Interior Noise

Noise is one of the key issues in the operation of high-speed railways, with sound source localisation and its transfer path as the two major aspects. This study investigates both the exterior and interior sound source distribution of a high-speed train and presents a method for performing the contribution analysis of airborne sound with regard to the interior noise. First, both exterior and interior sound source locations of the high-speed train are identified through in-situ measurements. Second, the sound source contribution for di erent regions of the train and the relationships between the exterior and interior noises are analysed. Third, a method for conducting the contribution analysis of airborne sound with regard to the interior noise of the high-speed train is described. Lastly, a case study on the sidewall area is carried out, and the contribution of airborne sound to the interior noise of this area is obtained. The results show that, when the high-speed train runs at 310 km/h, dominant exterior sound sources are located in the bogie and pantograph regions, while main interior sound sources are located at the sidewall and roof. The interior noise, the bogie area noise and the sound source at the middle of the coach exhibit very similar rates of increase with increasing train speed. For the selected sidewall area, structure-borne sound dominates in most of the 1/3 octave bands.

Active Source Seismic Identification and Automatic Picking of the P-wave First Arrival Using a Convolutional Neural Network

In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.

Study of engine noise based on independent component analysis

Independent component analysis was applied to analyze the acoustic signals from diesel engine. First the basic prin-ciple of independent component analysis (ICA) was reviewed. Diesel engine acoustic signal was decomposed into several inde-pendent components (ICs); Fourier transform and continuous wavelet transform (CWT) were applied to analyze the independent components. Different noise sources of the diesel engine were separated, based on the characteristics of different component in time-frequency domain.

Analysis of external noise spectrum of high-speed railway

A schematic to make the spectra of the exterior noise of high speed railway was put forward. The exterior noise spectrum was defined based on the characteristics of the high-speed train exterior noise. Its characteristics considered here include identifying the exterior main sources and their locations, their frequency components including the Doppler effect due to the noise sources moving at high speed, the sound field intensity around the train in high-speed operation, the sound radiation path out of the train, and the pressure level and frequency components of the noise at the measuring points specified by the International Organization for Standardization(ISO). The characteristics of the high-speed train exterior noise of the high speed railways in operation were introduced. The advanced measuring systems and their principles for clearly indentifying the exterior noise sources were discussed in detail. Based on the concerned noise results measured at sites, a prediction model was developed to calculate the sound level and the characteristics of the exterior noise at any point where it is difficult to measure and to help to make the exterior noise spectrums. This model was also verified with the test results. The verification shows that there is a good agreement between the theoretical and experimental results.

An investigation into high-speed train interior noise with operational transfer path analysis method

Passengers’demands for riding comfort have been getting higher and higher as the high-speed railway develops.Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis(OTPA)method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method,which has high test efficiency and can be carried out during the working state of the targeted machine.The OTPA model is established from the aspects of“path reference point-target point”and“sound source reference point-target point”.As for the mechanism of the noise transmission path,an assumption is made that the direct sound propagation is ignored,and the symmetric sound source and the symmetric path are merged.Using the operational test data and the OTPA method,combined with the results of spherical array sound source identification,the path contribution and sound source contribution of the interior noise are analyzed,respectively,from aspects of the total value and spectrum.The results show that the OTPA conforms to the calculation results of the spherical array sound source identification.At low speed,the contribution of the floor path and the contribution of the bogie sources are dominant.When the speed is greater than 300 km/h,the contribution of the roof path is dominant.Moreover,for the carriage with a pantograph,the lifted pantograph is an obvious source.The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation,and the contribution of air excitation is non-significant.Certain analyses of train parts provide guides for the interior noise control.

CEEMD-FastICA-CWT联合瞬态响应阶次的电驱总成噪声源识别

以某增程式电驱动总成为研究对象,提出基于联合算法的噪声分离识别模型。首先,采用互补集合经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)联合快速独立分量分析(fast independent component analysis,FastICA)方法提取纯电模式稳态工况下单一通道噪声信号特征,利用复Morlet小波变换及FFT对各分量信号时频特性进行识别。其次,采用阶次分析法和声能叠加法对稳态分量信号对应的各瞬态响应阶次能量进行对比分析,并结合皮尔逊积矩相关系数(Pearson product moment correlation coefficient,PPMCC)相似性识别确定不同噪声激励源贡献度。结果表明:减速齿副啮合噪声对该增程式电驱总成纯电模式运行噪声整体贡献度最大。

球形传声器阵列识别定位汽车内部异响声源

针对新款汽车在路面行驶时车内出现的异响问题,使用球形传声器阵列对汽车在不同车况下水泥路面行驶时汽车内部声场进行了测量。以球形传声器阵列测量的声压数据作为输入,运用球谐波束形成法,重构得出汽车内部声场分布,并对以阵列中心为球心的球面声场进行了可视化处理,从而实现了对异响声源的识别与定位。由声压频谱分析得出了汽车内部在750、1000和1500 Hz频率附近存在异响;通过对应的内部声场三维可视化结果,确定了异响声源的位置;在轮胎后部的车身加装橡胶挡板或海绵挡板隔离噪声后,异响得到了有效的降低。研究方法和结果有助于有针对性地控制与降低汽车行驶时车内异响以及改善车内声学环境。

基于波束形成的变压器噪声成像技术研究与应用

作为变电站中最为昂贵和重要的设备,变压器一直是科研人员的研究重点对象。目前对变压器噪声源识别的相关研究较少,因此结合波束形成技术针对变压器噪声源识别展开研究。首先根据所调研的变压器噪声特性,确定了所关注的噪声频段和所用的声源识别方法,阐述了宽带信号的波达方向估计与波束形成的相关理论,进而整理出了一种基于频域数据的宽带变压器信号定位思路,利用仿真结果证实了所提定位思路的合理性与准确性。最后基于LabVIEW平台搭建了一套变压器噪声源识别系统,并通过对不同情况下实际运行变压器的现场试验,对系统的定位效果进行了测试,其定位误差在2°左右,结果证明噪声源识别思路和搭建的系统平台具有良好的可靠性与可用性。

基于CEEMDAN-WSVD组合串扰消除法车内噪声源识别

为解决车内噪声源识别中结构路径易受外部因素干扰,以及多源振动串扰影响,导致采集的工况数据存在噪声等问题,提出基于自适应噪声的完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)的CEEMDAN-WSVD组合去噪法,该方法利用自适应加噪特征避免模态混叠现象发生,引入样本熵对高频含噪分量进行小波变换(Wavelet Transform,WT),实现一层降噪后进行重构;并采用奇异值分解(Singular Value Decomposition,SVD)对重构信号获取主分向量,同时使用主分量衰减方法剔除较小主分量,实现二层降噪。运用模拟仿真信号验证上述方法对复杂含噪信号有降噪效果。通过对采集的工况数据降噪处理,计算路径传递率并得到贡献量。将各降噪方法应用于工况传递路径模型中对比分析,发现经过本文方法降噪后模型的合成响应与实测响应准确性较高,降噪效果较优。

基于二级迭代的局部近场声全息研究及实验

为了有效减小传统近场声全息声场重建过程中的“窗效应”和准确利用有限测量孔径测量并定位潜艇的表面声源,提出一种基于二级迭代的局部近场声全息方法。首先,通过改进基于正交球面波的局部近场声全息方法,提出基于二级迭代的局部近场声全息方法;然后,仿真对比上述两种局部近场声全息方法的重构结果,研究外推误差和扩展测点数对重构精度的影响;最后,在消声水池中利用小孔径全息测量面进行基于二级迭代的局部近场声全息的实验研究。结果表明,基于二级迭代的局部近场声全息能大大减小有限测量孔径带来的“窗效应”和“孔径重复效应”误差,验证了该方法的优越性,可为水下大尺寸声源的近场声全息精确测量提供重要参考。

基于声阵列的电力设备强干扰噪声源识别系统设计

由于外界强噪声干扰导致电力设备不能正常运行,为此,基于声阵列设计了一种新的电力设备强干扰噪声源识别系统。采用SJI19210型采集器采集信号,通过SIK-99101型处理器拼接组合信号,利用SUIJNUHJ121型分析器内部的进场声全息分析模块完成噪声源分析。分别设定噪声分析程序和声阵列识别程序,利用声阵列的扫描因子确定噪声源,完成电力设备强干扰噪声源识别。实验结果表明,所设计系统对稳定波动噪声信号噪声源识别精度高于96.42%,对不稳定信号噪声源识别精度在95.25%以上,对相对稳定噪声识别精度为94.36%,具有极强的识别能力。

基于稀疏矢量声阵列的噪声源识别方法研究

【目的】为了提高质点振速的重建精度,保证声源识别的准确性,【方法】采用质点振速测量替代传统的声压测量,然后将矢量传感器与稀疏采样及稀疏正则化相结合,建立一个基于稀疏矢量声阵列的压缩等效源-近场声全息技术模型(CESM-v),并开展数值仿真和实验研究。通过质点振速重建、等效源源强重建、误差计算等多角度分析,将文章提出的CESM-v模型与传统的等效源-近场声全息技术(ESM)模型和现有的CESM-p模型进行对比。【结果】CESM-v模型总是优于另外两个模型,在稀疏采样条件下可获得媲美传统ESM充足采样条件下的质点振速重建效果,且比CESM-p模型具有更高更稳定的质点振速重建精度。仿真和实验结果均表明,CESM-v模型具有更好的稳定性和可靠性,在少量采样点条件下依然可获得较好的质点振速重建结果,从而保证声源识别的准确性。【结论】凭借优越的声场重构稳健性,CESM-v模型可推广至轨道交通等实际工程中,应用于噪声源识别及故障诊断。

基于计算机视觉的噪声源识别与声学特性分析

在现代社会,噪声污染已成为影响人类健康和生活质量的主要环境问题之一。计算机视觉作为一门利用计算机和算法解读视觉信息的科学,已广泛应用于图像处理、对象识别及场景解析等领域。在声学领域,结合计算机视觉可以实现对噪声源的视觉识别。首先介绍噪声源识别和声学特性分析的概念,其次详细阐述基于计算机视觉的噪声源识别过程和声学特性的综合分析方法,最后进行实验分析。

基于传递路径的加速行驶车外噪声源识别与改进

为降低某乘用车加速行驶车外噪声,通过加速行驶车外噪声试验采集目标点数据,分析目标点噪声曲线及车辆位移曲线,得出目标点最大噪声时的车辆位置;建立车辆加速行驶车外噪声主要声源部件到车外目标点传递路径模型,通过频谱分析噪声频率;利用传递路径分析判断主要噪声源,并对主要噪声源发动机进行优化。结果表明:最大噪声时车辆位于超过测试场地中心线4.6 m处,最大噪声源为发动机,噪声频率为140~180 Hz;将发动机转速为4500 r/min时转矩上升初始值减小后,车外噪声降低了2.6 dB,满足标准限值要求。传递路径分析方法可为加速行驶车外噪声源识别和改进提供参考。

定频滚动转子式压缩机储液器的降噪研究

针对某气候类型为T3的定频空调器在制热工况下的室外机噪声OA差值较小问题,通过对室外机的噪声频谱及振动传递路径分析,确定该问题为滚动转子式压缩机的八倍频和十二倍频噪声峰值过高所致。运用有限元分析与固频测试方法进行声源识别,得到464 Hz和696 Hz对应的峰频特征分别为压缩机储液器的声腔模态和结构模态,给出储液器的降噪优化方向,并进行试验验证。结果表明,在室内机不同风挡下,压缩机储液器优化后的空调器室外机右侧噪声在0~6kHz内最高峰值降低2~9dB(A),OA差值得到明显改善。

柴油机薄壁件噪声源识别及降噪研究

针对某柴油机薄壁件辐射噪声问题,以气阀罩盖为研究对象,测试与仿真相结合实现结构传递噪声与透射噪声的识别与分离。基于参数型修正法优化的有限元模型,分别以采集的模态扩展线框测点振动信号和螺栓处加速度信号为输入计算振动及声学响应并进行噪声源识别;通过隔声识别法试验验证噪声源识别结果的准确性;并研究气阀罩盖采取隔声减振与吸声措施时对整机噪声的影响。结果表明:模态扩展法与隔声识别法的噪声源识别结果一致,在400~4000 Hz频段以透射噪声为主,且透射噪声占比约80%;气阀罩盖采用隔声减振措施时,在内侧贴复合板的效果要优于外侧;采用轻量化低噪声复合气阀罩盖时,整机可降低3.0 dB(A)。为发动机薄壁件噪声源识别及降噪提供新思路。

基于RVMD-RobustICA-ST联合相干性分析的电驱动总成噪声源识别

为研究集成一体化电驱动总成的噪声源特性,提出一种RVMD-RobustICA-ST联合算法融合相干性分析的噪声源识别方法。首先,采用基于奇异值分解的占优特征值准则估计噪声子空间维数对变分模态分解参数进行指向性条件约束,并利用鲁棒性独立分量分析联合占优特征值约束的变分模态分解对信号特征进行提取。然后,利用S变换和快速傅里叶变换对各分量信号时频特性进行识别。最后,在二次残差法分析分量信号波形误差度基础上,以电驱动总成振动信号、噪声信号、时频重叠分量信号为变量建立线性系统并进行相干性分析。结果表明,稳态工况下减速二级齿副啮合振动噪声对该电驱动总成噪声贡献度最大,且时频重叠分量信号的噪声能量主要由减速一级齿副啮合振动提供。

锯齿尾缘构型降低圆柱-叶栅干涉噪声的实验研究

通过在叶栅上游放置圆柱产生各向异性的湍流尾迹,并与下游叶片相互干涉产生干涉噪声,研究了仿生学锯齿尾缘构型对圆柱-叶栅干涉噪声的影响。针对西北工业大学航空叶轮机气动力学与气动声学实验室(TAAL)低压涡轮平面叶栅,在不同来流速度的情况下,使用远场圆弧阵列测量了叶栅远场噪声,并基于CLEAN-SC数据处理算法使用由31个传声器组成的线阵列测量识别了不同叶栅的尾缘噪声,得到了圆柱尾迹来流下锯齿尾缘构型的降噪效果。实验结果表明,不同锯齿尾缘构型的降噪效果存在差异,当来流为圆柱尾迹时,对叶栅尾缘噪声的识别测量结果显示此时锯齿尾缘降噪效果有限,最佳降噪量仅为1.296dB,当来流速度较高时,采用的锯齿尾缘构型无法降低圆柱-叶栅干涉尾缘噪声。