A Simplified Formulation to Estimate Influence of Gearbox Parameters on the Rattle Noise

Occurrence of gear rattle in transmission systems can result in severe vibration and noise,which in applications such as automobiles is an important source of user discomfort.As a result,the reduction of the rattling noise has attracted lot of concerns.The rattling noise level is affected by several gearbox parameters,an understanding of which is essential to prevent the expensive design modifications at later stages of product development.To develop such understanding at the gearbox design stage,this paper analytically evaluates the gear parameters’effect on the root mean square of the wheel gear acceleration under idling condition,which is known to be linearly correlated to the rattling noise level.Therefore,this evaluation allows for an investigation of the gear parameters’influence on the rattling noise as well.This method is then verified by comparing the analytical results with the simulation results from a dynamic model built in SIMPACK as well as previously published experimental results.Thus,the proposed analytical evaluation method can optimize the gearbox specifications at the design stage to reduce the gear rattle noise level.

Micro-process model of hydraulic shock absorber with abnormal structural noise

In order to discover the causes of the abnormal noise of shock absorbers, it is necessary to identify the operating characteristics of the shock absorbers. A micro-process model for operation of the hydraulic shock absorber was presented. A novel concept, which describes the process of hydraulic shock absorber by dividing it into smaller steps, was proposed. The dynamic model and the differential equations were established. The results of numerical simulation agree well with data obtained from the vibrostand test, indicating that the collision between the piston and the oil, the alternation of static friction and sliding friction acted between the piston and the cylinder, and the adherence between valve plate and piston result in impact on the piston head near the top dead center and the bottom dead center. Ultimately, the impact excites the high-frequency vibration of the piston structure, which can generate the abnormal noise in the hydraulic shock absorber after its transfer. And the maximum vibration acceleration on the piston head and the abnormal noise increase with the increase of the gap between the oil and piston rod head, the maximum static friction force and the adhering function, respectively.

Effect of integral squeeze film damper on vibration and noise of spur gear with center-distance error

When the actual installation center distance between a pair of spur gears is greater than the theoretical center distance,backlash increases,leading to increased vibration and noise.The structural parameters of an integral squeeze film damper(ISFD)were designed with the stiffness of rigid support as reference to investigate the effect of an ISFD on the dynamic characteristics of a spur gear transmission system with center-distance installation error.A spur gear test bench with center distance-error was built to investigate the vibration and noise reduction characteristics of ISFD.The experimental results indicate that,compared with a rigid support,the ISFD can reduce vibration by approximately 40%and noise by approximately 5 d B.ISFD can effectively absorb the impact energy caused by an increase of in backlash,which is conducive to the stable operation of the spur gear transmission system.

Research on Real-time Monitoring of Abnormal Seismic Noise

The noise data in vertical component records of 85 seismic stations in Fujian Province during 2012 is used as the research object in this paper. The noise data is divided into fiveminute segments to calculate the power spectra. The high reference line and low reference line of station are then identified by drawing a probability density function graph( PDF)using the power spectral probability density function. Moreover, according to the anomalies of PDF graphs in 85 seismic stations,the abnormal noise is divided into four categories: dropped packet, low noise, high noise, and median noise anomalies.Afterwards,four selection methods are found by the high or low noise reference line of the stations,and the system of real-time monitoring of seismic noise is formed by combining the four selection methods. Noise records of 85 seismic stations in Fujian Province in July2013 are selected for verification,and the results show that the anomalous noise-recognition system could reach a 90% success rate at most stations and the effect of selection are very good. Therefore,it could be applied to the seismic noise real-time monitoring in stations.

On the Transmission Noise of Machine Gears and an Effective Solution

Noise pollution has become one of the world＇s three major pollutions. In modern industrial enterprises, the noise from gear transmission is an important source of pollution. In this paper, we first analyse the noise of a gear transmission system. Then, through＂ repairing the margin of gear, using shaving processing, shaving cutter grinding to present an effective solution.

Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

It is a challenging issue to detect bearing fault under nonstationary conditions and gear noise interferences. Meanwhile, the application of the traditional methods is limited by their deficiencies in the aspect of computational accuracy and e ciency, or dependence on the tachometer. Hence, a new fault diagnosis strategy is proposed to remove gear interferences and spectrum smearing phenomenon without the tachometer and angular resampling technique. In this method, the instantaneous dominant meshing multiple(IDMM) is firstly extracted from the time-frequency representation(TFR) of the raw signal, which can be used to calculate the phase functions(PF) and the frequency points(FP). Next, the resonance frequency band excited by the faulty bearing is obtained by the band-pass filter. Furthermore, based on the PFs, the generalized demodulation transform(GDT) is applied to the envelope of the filtered signal. Finally, the target bearing is diagnosed by matching the peaks in the spectra of demodulated signals with the theoretical FPs. The analysis results of simulated and experimental signal demonstrate that the proposed method is an e ective and reliable tool for bearing fault diagnosis without the tachometer and the angular resampling.

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies.However,some micro-motors may exhibit design deficiencies,component wear,assembly errors,and other imperfections that may arise during the design or manufacturing phases.Conse-quently,these micro-motors might generate anomalous noises during their operation,consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers.Automobile micro-motors exhibit a diverse array of structural variations,consequently leading to the manifestation of a multitude of distinctive auditory irregularities.To address the identification of diverse forms of abnormal noise,this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network(VAF-CNN).This method entails the deployment of distinct network branches,each serving to capture disparate features from the multi-sensor data,all the while considering the auditory perception traits inherent in the human auditory sys-tem.The intermediary layer integrates the concept of adaptive weighting of multi-sensor features,thus affording a calibration mechanism for the features hailing from multiple sensors,thereby enabling a further refinement of features within the branch network.For optimal model efficacy,a feature fusion mechanism is implemented in the concluding layer.To substantiate the efficacy of the proposed approach,this paper initially employs an augmented data methodology inspired by modified SpecAugment,applied to the dataset of abnormal noise sam-ples,encompassing scenarios both with and without in-vehicle interior noise.This serves to mitigate the issue of limited sample availability.Subsequent comparative evaluations are executed,contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data.The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference.Moreover,it holds notable practical significance in the engineering domain,as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.

NOISE OF THE RING SPINNING FRAME AND ITS CONTROL

In this paper, we have made a study on the noise of ring spinning frame by analysing the noise radiating mechanism and the frequency spectrum of its main noise sources, such as the spindle system and the transmission gear trains of the ring frame. Finally, three measures for reducing its noise are suggested: (1) using upper elastic bearing for the spindle structure, (2) using vibration isolating washers for the spindle support, and (3) improving the gear transmission structure design in the headstock of the frame.

Fast and robust strain signal processing for aircraft structural health monitoring

This work elaborates a fast and robust structural health monitoring scheme for copying with aircraft structural fatigue.The type of noise in structural strain signals is determined by using a statistical analysis method,which can be regarded as a mixture of Gaussian-like(tiny hairy signals)and impulse-like noise(single signals with anomalous movements in peak and valley areas).Based on this,a least squares filtering method is employed to preprocess strain signals.To precisely eliminate noise or outliers in strain signals,we propose a novel variational model to generate step signals instead of strain ones.Expert judgments are employed to classify the generated signals.Based on the classification labels,whether the aircraft is structurally healthy is accurately judged.By taking the generated step count vectors and labels as an input,a discriminative neural network is proposed to realize automatic signal discrimination.The network output means whether the aircraft structure is healthy or not.Experimental results demonstrate that the proposed scheme is effective and efficient,as well as achieves more satisfactory results than other peers.

齿轮传动振动噪声计算的Kato方法

建立了齿轮传动的时变动力学模型 ,分析了啮合刚度、传动误差对振动响应的影响 ,在此基础上 ,将齿轮传动啮合分成单齿啮合区和双齿啮合区 ,采用Kato方法对振动产生的噪声进行了定量计算 ,得出了双齿啮合区内辐射噪声大于单齿啮合区 ,并给出了每一啮合时刻辐射噪声的大小。研究表明该方法可以较好地对齿轮振动所产生的噪声进行定量计算 。

基于多传感数据融合的变速运行齿轮异常振动故障诊断

变速运行齿轮异常振动故障诊断性能过差会增加汽车维护成本,缩短齿轮使用寿命。为了及时识别齿轮故障,保证汽车变速器总成具有良好的振动特性,提出基于多传感数据融合的变速运行齿轮异常振动故障诊断方法。通过分析多传感器数据融合技术,掌握变速运行齿轮异常振动故障诊断的理论框架,并以此为基础,参考传感器融合模块、特征级并行多神经网络局部诊断模块和终端分类模块,结合变分模态分解、多通道加权融合和单隐层前馈神经网络训练算法,从信号采集、信号特征提取和信号特征分类3个步骤实现变速运行齿轮异常振动故障诊断。实验结果表明:在齿轮发生轻度磨损时,磨损振动信号的幅值在20~40 mV之间,磨损振动信号的频率在0~4000 Hz区间;中度磨损时,信号的幅值在30~55 mV之间,信号频率在3000~7000 Hz区间;重度磨损时,信号幅值在50~70 mV之间,信号频率在6000~12000 Hz区间,且各阶段诊断结果均与故障程度的实际转折点吻合。由此可知在各样本数量均相同的情况下,提出的故障诊断方法预测值与真实值均相同,故障程度和故障类型的诊断性能均较好。

基于多特征提取与灰狼算法优化SVM的车内异响识别方法

传统的异响识别方法对测试设备要求较高且易受实验员经验差异影响。针对这种情况,提出一种基于多特征提取与灰狼算法优化支持向量机(Support Vector Machine,SVM)的车内异响识别方法。该方法以采集实验获得的6种车内常见异响作为研究对象,提取短时能量、小波变换优化的梅尔频率倒谱系数(DWT-MFCC)及其一阶差分组成混合特征参数,将灰狼优化算法应用于SVM的参数寻优中,建立异响识别模型并进行识别分类,同时探究选用不同维度的特征或不同算法对识别效果的影响。结果表明,所提取的25维混合特征能有效传达异响信息,该方法在收敛速度与识别准确率方面优势明显,能更好地实现车内异响的识别。

基于CETOL 6σ的空调器齿轮传动机构异响整改分析

空调导风机构主要通过步进电机及齿轮传动机构带动下运动,本文通过CETOL 6σ软件结合设计规范对齿轮传动机构零件及三维公差进行深入分析,解决导风机构运行异响,识别齿轮传统机构零件及设计容易失效点,制定相应预防措施,提高空调器运行可靠性及舒适性。

基于MATLAB的六极并联齿轮泵优化设计及分析

针对普通外啮合齿轮泵流量脉动品质差、质量大、成本高等问题,设计一种六极并联齿轮泵。分析六极并联齿轮泵的结构及其工作原理;对六极并联齿轮泵的瞬态流量特性进行分析,推导其单周期内的流量曲线函数;建立基于流量脉动系数、流量脉动频率和泵体体积的数学模型,选取设计变量,确定约束条件,并利用MATLAB优化工具箱中fmincon函数对目标函数进行参数优化。最后对相同理论流量和额定进出口压差下的六极并联齿轮泵及普通外啮合齿轮泵进行瞬态流量仿真和齿轮泵特性计算,并将结果进行对比分析。仿真结果表明:在结构设计合理的情况下,六极并联齿轮泵在减小流量脉动,降低振动、噪声、质量和制造成本,提高工作性能和使用寿命方面具有重要作用。

某商用车怠速油底壳异响问题分析

针对产品开发过程中某柴油车型发动机油底壳怠速时“呜呜”异响问题,通过噪声与振动的测试及仿真分析,对油底壳异响原因进行分析。结果表明,怠速时机油泵的工作激励频率与油底壳模态频率耦合,形成“呜呜”声,并通过油底壳表面辐射出来。进一步实验研究表明,随着机油温度的升高,机油泵功率、工作激励幅值会逐步减小,异响逐步减小。同时实验研究表明,机油量对油底壳模态频率有影响,油底壳内机油加注量越多,油底壳模态频率越低,油底壳表面振动幅值及异响均逐步减小。针对该异响问题,明确该发动机机油加注上限及下限时油底壳对应频率范围后,通过优化怠速转速,提高油底壳模态频率等使机油泵激励频率与油底壳模态频率避频,解决了该异响问题。

大型客机机体噪声机理及控制技术

气动噪声问题不仅是大型客机适航取证的关键技术难题,更是关系到乘客舒适度的重要技术指标。增升装置噪声和起落架噪声是大型客机机体噪声的主要来源。本文针对我国大型客机研制中增升装置噪声和起落架噪声的产生机理及控制关键科学问题,开展噪声源、远场声辐射精细化数值模拟。针对噪声产生机理,发展了近壁面边界涡量流、拟涡能流等涡动力学过程与涡声理论相结合的噪声源分析方法;针对机体噪声控制难题,提出了缝翼凹腔小尺度波纹噪声控制方法和起落架支杆大尺度波纹噪声控制方法。数值模拟结果表明:缝翼凹腔是增升装置的重要噪声源之一;中低频噪声和纯音峰值主要来自缝翼凹腔和主翼前缘,后缘襟翼对噪声的贡献相对较小;起落架尾迹区域和轮胎空腔区域存在大量、复杂的大尺度湍流涡结构,是起落架的主要噪声源,起落架各部件噪声特性基本呈现宽频特性,支柱部件辐射噪声最大,轮胎次之,中间的连轴部件和扭杆部件辐射噪声相对较小;缝翼凹腔波纹噪声控制方法兼具减阻和降噪功效;起落架大尺度波纹噪声控制方法能有效降低远场辐射噪声。本文研究内容可为增升装置气动与噪声一体化设计、起落架降噪设计等提供技术支撑。

半监督TS网络模型在齿轮故障诊断中的应用

为解决在工业大数据条件下,有标签样本少导致机械故障诊断准确率低的问题,提出了一种半监督神经网络模型。该方法采用协同训练的方式,从时域和频域两个维度训练教师网络(T),将无标签数据转化为高质量的伪标签数据。再利用转化后的伪标签数据训练学生网络(S),通过对数据进行评判和计分,避免网络过拟合。最后通过得分函数,对伪标签数据进行阶梯筛选成为有标签数据。齿轮故障诊断结果表明:TS网络在仅有少量有标签数据的情况下,故障分类准确率达90.31%,与其他半监督方法相比,准确率高出15%~20%。在信噪比(SNR)为5、0、-5的条件下,模型可以达到86.81%、78.00%、52.78%的诊断准确率。

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。

强噪声干扰下采煤机行星齿轮故障诊断方法

采煤机摇臂截割部行星齿轮的健康状态直接影响截割效率。针对采煤机截割煤岩过程中受多重冲击引起的强噪声干扰、齿轮结构复杂且传递路径多变导致故障特征难以提取等特点,提出了一种基于频谱平均降噪和相关谱的采煤机行星齿轮故障诊断方法。根据信号频谱分布特征及噪声随机特性,采用频谱平均降噪方法抑制噪声对信号频谱的干扰,获得信号降噪频谱。构建相关谱以建立少样本降噪频谱和多样本降噪频谱的内在联系,减少频谱平均降噪对样本数量的需求。采用一维卷积神经网络(1D CNN)建立相关谱与故障类别之间的精确映射关系,以相关谱为输入、故障类别为输出,实现行星齿轮故障分类识别。在DDS传动系统故障诊断实验台对基于频谱平均降噪和相关谱的采煤机行星齿轮故障诊断方法进行实验验证,结果表明该方法能够增强表征故障特征的关键频率,对正常、断齿、磨损、缺齿和裂纹5种行星齿轮健康状态信号的整体识别率达96%,在信噪比不低于15 dB时可有效、准确地实现齿轮故障诊断。

低真空管道磁浮系统环境测试与数据处理

针对低真空管道磁浮交通运输系统搭建了数据采集系统,采用了GPS信号授时与IEEE1588以太网时钟同步的方式实现了分布式数据采集系统的时钟同步网络;对系统的多传感器进行了低真空和电磁场环境的影响测试,发现低真空环境下激光位移传感器产生了测量值减小的现象;同时以激光位移传感器为例,对比低真空环境下不同数据降噪方法对添加了不同信噪比的高斯噪声进行滤波处理,得出了不同信噪比下不同降噪方法的处理效果,以此为依据在工程实际情况选出最合适的降噪方法来得到更好的降噪效果;最后采用了基于不同降噪方法差值的数据异常值处理方法,与普通的区间估计异常值处理方法对比,从跳点去除效果和总去除个数证明了基于小波降噪的差值处理法对异常值处理的有效性,满足了实际工程的需要。