强脉冲干扰非线性混叠转子故障信号分离

由于旋转机械在运行过程中,传感器测得的振动信号是各振源的混叠信号且含有很强的噪声,常规的信号处理方法难以分离混叠信号,对设备的状态监测和故障诊断造成了很大的困难。针对这一问题,介绍了盲源分离基本原理和方法,指出源分离算法在脉冲噪声环境下失效。针对强脉冲噪声环境下的混叠振动信号,首先,通过中值滤波降噪方法对振动信号进行降噪;然后,通过盲源分离算法对降噪后的信号分离;最后,利用该方法对实测混叠转子振动信号成功实现了降噪和故障信号分离。仿真结果验证了提出方法的有效性。     

基于独立分量分析的消噪方法在旋转机械特征提取中的应用

为了有效提取信号特征，为后续的故障诊断分析提供足够的信息，针对旋转机械故障诊断的特点，探讨了在旋转机械振动信号采集过程中基于独立分量分析方法的降噪措施的可行性，并在此基础上，研究了基于独立分量分析的消噪方法在旋转机械升降速过程中信号特征提取中的应用。实验结果表明，利用该方法可有效消除振动信号采集过程中混入的噪声。     

振动信号频域积分的滤波修正算法

机械故障诊断中,通过振动信号的软件积分可以实现振动加速度、速度信号向位移信号的转换。为了消除信号中噪声对软件积分结果的影响,分析了软件积分在振动信号积分中趋势项产生的原因,提出了基于传感器低频滤波与阀值滤波修正的频域积分算法。方法简单实用,消除了软件积分结果中的摆动趋势。数值仿真结果与实测结果表明,方法抗噪声能力强,具有很好的精度和通用性,目前已经应用于旋转机械状态监测与故障诊断系统中,为判断机组运行状态提供了很好的依据。     

基于EEMD和包络分析的滚动轴承故障诊断研究

在较大的背景噪声中,对旋转机械进行监测,包络分析不能从测得的振动信号中解调出故障。基于旋转机械振动信号具有非平稳、非线性特点,提出一种新的基于EEMD和包络分析的滚动轴承故障诊断方法,该方法首先运用EEMD方法将振动信号分解成有限个本征模态分量和残余量,对这些分量进行包络分析,可以解调出故障。通过仿真信号和实际滚动轴承外圈的振动信号分析表明该方法能够有效地识别滚动轴承的故障。     

EMD方法的改进研究及其在机械信号中的应用

机械振动或声学信号的处理是状态监测及故障诊断的基础。探讨了经验模式的分解特性,将相干分析应用于判定分解中虚假分量,以避免虚假分量和微弱信号的误判;其次研究了信号各频率能量比例对EMD分解时虚假分量的影响,提出了增加大幅值的高频分量可以减小虚假分量比例的方法;然后基于白噪声的EMD分解特性,提出了以叠加多次有色噪声改善EMD模式混叠的方法,同时仿真分析了其优越性,最后将改进的EMD系统方法应用于旋转机械噪声和排气系统振动信号的特征提取,验证了新方法的可行性。     

浅析振动频谱分析技术在旋转机械故障诊断中的应用

本文阐述了利用振动频谱分析技术对振动信号进行分析与处理,实现对旋转机械的状态监测与故障诊断。此方法在实际应用过程中为设备维护提供了重要依据,并取得了良好的使用效果和经济效益。     

基于LabVIEW的旋转机械振动状态监测及故障诊断系统设计

采用虚拟仪器技术对旋转机械的振动信号进行数据采集以及状态监测,并通过运用数字信号分析方法对转子振动信号进行分析,通过分析其绘制的图谱实现对旋转机械的故障诊断。     

基于改进多元经验模态分解的多通道振动信号融合分析

振动信号是机械故障诊断的主要信号之一,单一信号获取信息量有限,抗干扰能力较差,多通道数据比单通道数据获得了更完善的机械健康状态。本文提出了一种自适应噪声辅助多元经验模态分解方法实现多通道振动信号的同步分析。在多通道信号的基础上添加两个噪声辅助通道,以原始信号多通道加权正交指数最小为目标,通过自适应权重粒子群算法搜索最优K(投影向量个数),α_1,α_2(两个辅助噪声通道的噪声强度)最优参数组合,实现多通道自适应同步分析。改进的方法提高了分解精度,有效抑制模态混叠。仿真实验和工程案例验证了该方法的有效性,与经验模态分解和多元经验模态分解相比,自适应噪声辅助多元经验模态分解方法提高了分解精度,能准确地提取旋转机械故障频率。     

一种基于冲击信号聚类分析的轴承故障诊断方法

为了对旋转机械内轴承的运行状态进行故障监测和诊断,在对振动冲击信号进行分段截取的基础上,提出了基于分段信号时、频域特征提取结合模糊K聚类的滚动轴承故障诊断方法,并将该方法应用于NU205轴承故障诊断中。     

基于Bi-LSTM和自注意力机制的旋转机械故障诊断方法研究

旋转机械因其特殊的功能要求,通常工作在恶劣的环境中,振动信号易受外界干扰。基于传统信号处理方法的故障诊断技术越来越不能满足故障诊断精度的需要,因此,利用大数据和人工智能技术进行旋转机械故障诊断成为目前的主要研究方向之一。针对以上问题,提出一种基于双向长短时记忆网络（Bi-LSTM）和自注意力机制的旋转机械故障诊断方法。首先,利用转子实验台模拟旋转机械的各种运行状态,采集旋转机械在各种运行状态下的振动信号;然后,将振动信号输入Bi-LSTM网络,自注意力机制将Bi-LSTM各时间步的输出进行加权求和,获得振动信号的深层特征表示;最后,通过全连接层和Softmax层输出旋转机械各种运行状态的预测概率。实验结果表明：本文提出的方法能够有效地实现旋转机械的故障诊断,与其他方法相比,模型的训练稳定性、收敛速度和故障识别准确率均得到提高。     

工程机械机外噪声声源分析及降噪处理

某型全液压抓料机的机外噪声较大,需确定其噪声源并进行降噪处理.分别测量机外前、后、左、右4个方位噪声的声压信号并进行频谱分析,采用多方位噪声信号分析、机器工作特性和机器结构布置相结合的方法判断噪声源.结果表明,机外噪声主要由风机运转噪声和发动机排气噪声组成.根据两噪声源各自的频率特性及机器的结构布置特点,采用微穿孔板共振吸声结构作为降噪措施,分别设计并安装了消声器和隔声罩,抓料机外噪声下降了5.8 dB,达到了欧洲最新噪声限制标准的要求.     

Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

Under the only hypothesis of independent sources, blind source separation (BSS) consists of recovering these sources from several observed mixtures of them. As it extracts the contributions of the sources independently of the propagation medium, this approach is usually used when it is too difficult to modelise the transfer from the sources to the sensors. In that way, BSS is a promising tool for non-destructive machine condition monitoring by vibration analysis. Principal component analysis (PCA) is applied as a first step in the separation procedure to filter out the noise and whiten the observations. The crucial point in PCA and BSS methods remains that the observations are generally assumed to be noise-free or corrupted with spatially white noises. However, vibration signals issued from electro-mechanical systems as rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA.This paper extends a robust-to-noise technique earlier developed for the separation of rotating machine signals. It exploited spectral matrices of delayed observations to eliminate the noise influence. In this paper, we focus on the modulated sources and prove that the proposed PCA is available to denoise such sources as well as sinusoidal ones. Finally, performance of the algorithm is investigated with experimental vibration data issued from a complex electro-mechanical system.     

基于鲁棒预白化的机械噪声信号盲分离

机械噪声故障诊断的难度在于实际检测的噪声是多个设备或零部件噪声信号的混合,信噪比低,基于二阶统计量盲源分离算法的故障噪声诊断技术,利用二阶协方差矩阵的联合对角化,从测量噪声中分离出感兴趣故障噪声进而提取特征,但该算法抗干扰噪声性能差。本文利用多个协方差矩阵平滑滤波后的矩阵进行白化,进一步提高了抗干扰噪声能力,在样本数据较少时仍能实现较好的盲源分离效果,仿真实验证实了该算法的有效性。     

声源定位法在检测机械故障位置中的应用

针对旋转机械发出的具有较强噪声的周期性脉冲信号,提出了一种能够找出发出声音脉冲信号的位置的声源定位方法,并将这种方法应用到确定机械设备故障所在的位置中去.仿真结果表明,在具有较强噪声干扰的情况下,该方法能够有效地确定脉冲声源的位置.     

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 .     

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.     

BLIND SOURCES SEPARATION APPLIED TO ROTATING MACHINES MONITORING BY ACOUSTICAL AND VIBRATIONS ANALYSIS

Blind Sources Separation (BSS) is a promising technique for signal processing and data analysis that allows the recovery of unknown signals (called sources) from observed signals mixed by an unknown propagation medium. The adjective ‘blind’ indicates that no assumption was done on the signals and the mixture. This lack of knowledge is compensated by assuming the independence of the source and the linearity of the propagation medium. We apply this technique to a test bench where various machines operate simultaneously in order to diagnose each element. So, by applying the superposition principle, the signals measured by every sensor positioned on each machine, are disrupted by the influence of other signals from the surrounding machines of the factory. Our goal was to remove the influence of the other machines, without having to stop them, which would be damaging for production. BSS methods provide an interesting alternative, since they permit in theory to solve our problem, i.e. to restore on every sensor the signature of its own machine. The sensor signals were assumed to be a convolutive mixture of independent signals arising from different physical phenomena. The method used is based on the N'guyen Jutten algorithm initially developed to separate speech signals. The algorithms used were adaptive and worked on the measured temporal signals from accelerometers or microphones. The signals were produced on a test bed carrying two low-power d.c. motors fixed to the same structure, whose speed of rotation could be varied. The signature received by each sensor therefore contained the contributions of the two motors. The results indicate that this approach can be successfully applied to these signals for vibration analysis; acoustical analysis is more complex and will be discussed in detail.     

Multi-dimensional spectral analysis of the noise contribution from a drum washer with a dehydrating condition

Recently, there has been a growing consumer interest in the amount of noise produced by household electrical appliances. The designer of the product must determine the source of the noise, in order to eliminate the source. In the case of a household electric appliance such as the washing machine, the consumer’s complaint was about the noise that is generated during the dehydrating condition. However, in the case of the washing machine, it is difficult to identify the noise source when the washing machine uses the dehydrating condition. Several noise sources combine making it difficult to determine the key factor that contributes to the noise output. Multi-Dimensional Spectral Analysis (MDSA) is a method that can remove the correlation between different noise sources, and it expresses the key contributing factor as a unique output. This study utilized MDSA to analyze the contribution of each noise source in the output during the dehydrating condition.