波叠加法在机械噪声故障特征提取中的应用研究

声学故障诊断中,测量到的噪声信号是现场所有声信号的混合.为提取待诊设备噪声故障特征,建立机器系统的多声源宽带相关混合声场模型,使用波叠加法重建源表面为任意形状的空间声压场分布,计算出未知声源的数目与位置.提出的算法具有计算速度快、重建精度高,能够消除其他噪声源信号的干扰,从较小的信噪比的观测信号中分离待监测源信号的功率谱,有效提取机械噪声故障特征.实验结果验证模型与算法的可行性.     

安检设备辐射噪声分析与控制

以某型安检设备为研究对象,进行噪声振动测试分析,获得噪声和振动频谱。通过理论分析和计算确定噪声频谱图中各噪声峰值对应的噪声源及其传播途径,并同步采集主要噪声源部件的振动加速度信号。对振动和噪声信号进行频谱分析及相干性分析,指出电动机为该安检设备的主要噪声源,在初步采取减振、降噪措施后,噪声下降3．9dB（A）。     

护风圈对发动机冷却风扇气动性能影响的CFD分析

本文采用Realizable k-ε湍流模型对发动机冷却风扇进行CFD模拟,计算了转速2000r/min时的风扇流量,研究护风圈对其气动性能的影响。在此基础上,利用宽带声源模型中的Curle噪声源模型对风扇流场中的噪声源分布进行了分析,并通过瞬态仿真分析护风圈对风扇噪声频率特性的影响。分析结果表明:安装护风圈可以提高风扇11.6%的流量,同时可以降低气动噪声。     

如何测量微波相应噪声

高频信号主要受干扰及热噪声，散弹噪声和鉴频器引起的噪声等内部噪声的影响。那么，影响微波设备正常工作的主要噪声源是什么，这种噪声源又是怎样形成的，怎样测量，怎样找出一种消除噪声影响的方法。本文将围绕上述问题介绍微波系统噪声的形成机理，测量方法和测量电路。     

闭式柴油发电机组降噪研究

首先概述了闭式柴油发电机组的结构形式,然后针对某380kW闭式柴油发电机组噪声过大的问题,按国际标准对机组噪声进行了测试,结合柴油发电机组的声源特性和传播特点,通过频谱分析和声源分离方法,确定了发电机组的主要噪声源和声源贡献量。从噪声源本身和噪声传递路径两方面对噪声进行控制,采取降低风扇转速、更换减振垫、增加吸音棉厚度和改善消声器性能等降噪措施,使机组噪声下降显著,声功率级降低至98分贝,最终满足欧盟法规要求。     

基于声强测量的设备噪声源定位

为了定位设备噪声源,用声强测量方法测量了汽轮鼓风机和射流抽汽器的声强.采用声功率排序法对所测设备的噪声源进行了排序.结果表明:汽轮鼓风机是主要噪声源,鼓风机部分是汽轮鼓风机的主要噪声源.声强测量方法是多声源噪声系统中定位设备噪声源的有效方法.     

运动噪声源的时域传递路径模型及贡献率分析

运动物体声源贡献率的识别是有效降低其噪声的关键。针对车辆加速行驶过程中,噪声源随车运动、位置和速度实时动态变化的特性,提出一种新的运动声源的时域传递路径识别方法。利用脉冲响应函数代替常用的传递函数对系统的时变特性进行分析和研究,根据车辆的动态位置和速度,采用非线性时间变换的方法消除信号在传播过程中的多普勒效应对响应信号的影响。建立时域识别理论公式,通过标准声源试验,比较时域识别、频率识别精度,并详细分析实际客车加速行驶噪声源识别效果。通过标准声源试验及实际应用结果说明,时域传递路径分析法较频率分析法具有较高的分析精度和更高的计算效率。     

四车道高速公路交通噪声模型仿真分析

目前,国内外高速公路交通噪声预测模型中大多使用等效连续A声级来进行噪声预测,并且在高速公路交通噪声的仿真分析中使用一条线声源来模拟噪声源,与实际情况有一定差别。该文应用声学仿真软件,建立了四车道高速公路交通噪声模型,并从频域的角度,计算了在单声源、双声源两种预测模式下,距离路肩20m、40m、80m、160m处四个受声点的声压级,分析了两种声源模式下受声点的声压级频谱,得到了在四车道高速公路模型中的两种声源预测模式下,距离路肩20m及以远处受声点各频率处的声压级差值不大于1.29d B,总声压级的差值不大于1.19d B的结论,为道路噪声控制和预测提供了参考依据。     

机械噪声故障特征提取的波叠加法

建立了机械故障特征提取的声学理论模型,使用了波叠加法重建源表面为任意形状的空间声压场分布,计算出未知声源的数目与位置。提出的算法计算速度快、重建精度高,能够消除临近机器或部件辐射噪声的干扰,从较小的信噪比的观测信号中分离待监测源信号功率谱,有效提取了机械噪声故障特征。     

汽车暖通空调气动噪声仿真

为研究汽车暖通空调气动噪声特性,以某车型汽车暖通空调为研究对象,通过风阻试验对滤清器和蒸发器进行等效处理,建立仿真模型;基于流体动力学和气动声学理论,结合宽频带噪声源模型和FW-H模型,预测声源分布和噪声特性;将仿真结果与试验结果进行对标验证,出风口风量最大误差为6.7%,最小为2.1%,噪声频谱特性具有较高的一致性。结果表明,Curle噪声源主要分布在鼓风机和空调箱壁面,近场以旋转噪声为主,远场宽频噪声占主导。可适当通过减小叶片与空气接触面积、增加叶片数量、调整蜗舍角度和间隙、增加导流装置等措施降低暖通空调工作时产生的噪声,为汽车暖通空调前期开发提供一种研究方法。     

基于盲源分离技术的故障特征信号分离方法

信号采集过程中，传感器测量到的信号是实际振动信号在此测量方向的投影值，由于其他不相干振源的影响，测量信号由多个振动信号成分组成。在分析多振源信号混合模型的基础上，采用盲源分离技术分离不同的振源信号，讨论分离结果的广义初等相等性质的影响，研究估计振源数目的方法和选取测量信号的方法，利用二阶特征矩阵联合近似对角化算法，从测量信号中分离故障特征源信号。该算法可减小信号采集不当造成的影响，有效提高特征信号的提取。     

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars (BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is affected. Thus, a new multiple signal classification (MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare-bones particle swarm optimization algorithm (IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO-based MUSIC, is proposed by replacing the fixed-step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the effectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10^?5, and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO-based MUSIC is applied in BRB fault detection of an induction motor, and the effectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has sufficient frequency precision to detect BRB fault in induction motors.     

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars(BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is a ected. Thus, a new multiple signal classification(MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare?bones particle swarm optimization algorithm(IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO?based MUSIC, is proposed by replacing the fixed?step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the e ectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10~(-5), and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO?based MUSIC is applied in BRB fault detection of an induction motor, and the e ectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has su cient frequency precision to detect BRB fault in induction motors.     

可用于相干声源的快速反卷积声源成像算法

提出一种可用于相干声源识别的快速反卷积声源成像算法（Fast deconvolution approach for the mapping of coherent acoustic sources,FC-DAMAS）。该算法去除了反卷积声源成像算法（Deconvolution approach for the mapping of acoustic sources,DAMAS）中的互谱过程,直接求解声源复数源强分布,从而避免了互谱操作导致的待求未知数个数的剧增,因此不再需要采用非相干声源假设来减少待求未知数,使该算法能够同时适用于相干和非相干声源的识别;其次,该算法在反卷积求解过程中采用了与稀疏约束反卷积声源成像算法（Sparsity constrained DAMAS,SC-DAMAS）类似的L1范数稀疏约束反卷积方法,使算法在相干和非相干声源的识别过程中均具有很高的计算精度和空间分辨率;此外,该算法中增加了对测量声压的主成分分析去噪过程,弥补了取消互谱去噪过程造成的算法鲁棒性下降,使算法具有与SC-DAMAS算法类似的噪声鲁棒性。与现有可用于相干声源识别的反卷积声源成像算法（Deconvolution approach for the coherent sources,DAMAS-C）相比,提出的FC-DAMAS算法大大降低了待求解的矩阵方程规模,使其计算效率得到了显著提升。通过数值仿真和实验验证了FC-DAMAS算法的优越性,结果表明所提出的FC-DAMAS算法在应用范围、声源识别性能和实用性方面都更具优势,更适于在实际工程中应用。     

感应电机轴承故障检测方法研究

分析了感应电机轴承发生故障时的振动信号的特性,利用带通滤波器和希尔伯特变换,对感应电机轴承振动信号进行处理,然后采用高分辨率谱估计算法--MUSIC（Multiple Signal Classification）算法对包络信号作谱分析,再从包络信号的MUSIC谱中提取故障特征频率分量.研究结果表明,该方法频率分辨率更高,故障检测更为准确.将该方法应用于电机轴承故障诊断,可准确提取轴承故障特征分量.     

基于角域AR模型滤波的滚动轴承故障诊断

在齿轮噪源存在的变转速滚动轴承故障诊断过程中,因混合信号中转频分量相对较小,使得基于时频表达的阶比跟踪技术受到限制。虽然基于故障特征频率的角域重采样能提取轴承的故障特征,但这种算法不能确定故障位置,而且可能会出现误判。针对这一问题,提出了基于角域自回归(auto regressive,简称AR)模型滤波的处理方法。该方法利用线调频小波路径追踪算法从降采样处理的混合信号中提取齿轮瞬时啮合频率趋势线并估计转速,根据估计转速信息对原混合信号进行等角度重采样,获得了角域信号。利用角域信号中齿轮啮合振动成分具有周期性的特点,使用AR模型对其滤波,并且对滤波后信号进行包络阶比分析,完成故障判断。通过处理仿真信号和实验信号,验证了该方法不仅能有效地去除齿轮噪声,并且可以判断轴承故障位置。     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semianechoic chamber demonstrate the effectiveness of the presented methods.     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .     

基于稀疏特征的连轧机故障信号分离方法

连轧机组的稳定性对于保障轧制产品的质量精度起着决定性的作用,连轧机组中监测各轧机状态的信号具有强耦合性,从复杂的信号中分离出各轧机独立的状态信号,对连轧机组的状态监测和故障诊断具有重要的意义。提出了一种基于稀疏特征的连轧机故障信号分离方法,并进行了仿真和现场验证。首先,通过基于时频谱分割的稀疏分解方法将各混合信号中的微弱冲击特征提取出来;其次,对所有稀疏表示信号的原子按照一定规律排序,得到各混合信号的稀疏矩阵;然后,根据稀疏原子的相似性对稀疏表示的原子进行聚类,确定盲源分离的源个数;最后,根据稀疏矩阵的系数和源个数比较准确地估计出混叠矩阵,实现混合信号的盲分离。