基于声振耦合分析的离心泵故障诊断

离心泵故障诊断一般采用接触式传感器采集的振动信号作为分析依据,但由于某些泵组的结构复杂、工况特殊,有时难以采用接触式传感器采集任意位置的振动信号。故本文采用了非接触式的传声器采集故障泵组的声音信号,并与振动信号进行耦合分析,从而利用噪声信号诊断出故障位置,为离心泵故障诊断技术提供了一个新的思路与方法。     

变转速工况下基于多传感器信号深度特征 融合的电机故障诊断研究

本文提出一种利用多传感器信号深度特征融合的方法实现电机变转速工况下的故障诊断。首先从多传感器节点同步采集电机的多通道振动、声音和漏磁信号。对漏磁信号进行处理获取电机转子的累积转角曲线,随后利用累积转角曲线对振动和声音信号进行阶比分析处理。最后利用双层双向长短期记忆网络从经过预处理的多传感器信号中提取和融合特征以诊断电机故障。实验结果表明,通过提取和融合8通道的电机振动和声音信号,本文提出的方法能够有效识别电机的高阻接触、偏心、霍尔断线、相间短路、轴承等10类运行状态,分类准确率达到99.86%。该方法有望部署在物联网边缘计算节点中,实现电机的远程在线状态监测和故障诊断。     

液压泵信息融合故障诊断

针对液压泵故障特征的分散性和模糊性，提出基于振动和压力传感器的信息融合故障诊断方法。在充分分析液压泵球头松动故障机理的基础上，对振动信号和压力信号进行小波消噪处理，有效提取球头松动的故障特征。将不同类型特征参数进行特征层融合，利用主成分分析和改进算法的BP神经网络实现液压泵球头松动故障诊断。试验表明，基于不同类型传感器信息融合故障诊断方法可以有效地实现液压泵微弱故障的诊断。     

改进小波包降噪与RSSD的滑动轴承声音诊断方法

针对滑动轴承故障特征在声音信号中体现非常不明显，且成分复杂、受干扰严重的问题，提出改进小波包降噪与共振稀疏分解的滑动轴承声音诊断方法。利用改进小波包降噪方法对滑动轴承声音信号进行降噪。利用共振稀疏分解分离出故障冲击成分。对低共振分量进行包络分析，确定滑动轴承的故障特征频率。实验结果表明，文中方法能够实现利用声音信号进行滑动轴承的故障诊断，为滑动轴承的故障诊断提供了新的思路。     

大型电动机的状态监测及诊断

电动机故障包括绝缘故障、定子故障、转子故障、轴承故障等。各种故障都会以一定的故障信号方式表现出来，而通过对信号中故障特征信号的提取分析可以对电动机故障进行判断。本文对电动机的多种基于信号监测的故障分析方法进行了原理分析，包括对定子电流信号的多种分析、轴承振动的频谱分析、电动机转速的波动分析等，对其他的多种故障监测方法也进行了介绍，并对每种分析方法所适用的故障诊断类型及优缺点给予了说明，最后指出了今后的发展趋势，为电动机故障诊断方法的应用提供了参考依据。     

基于LabVIEW的柴油机故障诊断虚拟仪器开发

开发了一套基于分形理论,使用柴油机声音信号进行故障诊断的虚拟仪器.介绍了其硬件平台的搭建,结合LabVIEW与MATLAB混合编程阐述了软件平台的设计,该平台由声音信号采集、信号预处理、故障特征提取、故障诊断4个模块构成.在故障特征提取模块中对分形关联维数的G-P算法进行了论述.结合柴油机故障实例测试表明:柴油机声音信号关联维数随工作状况改变有明显变化,能作为故障诊断的特征量,通过该虚拟仪器能迅速有效地识别出故障.     

基于经验模态分解算法的电机故障判断方法

为了提高电机故障诊断的效率,实现自动故障诊断,本文利用EMD算法对电机运行时的声音信号进行了特征提取。首先,对声音信号进行EMD分解,得到多阶IMF分量;然后计算出每阶IMF分量的特征值,从而提取出了电机故障声音信号的特征向量。从实验中可知,EMD可以根据信号自身的特点进行自适应分解,使得电机正常和故障时的特征具有明显的差异性,利于分类器进行分类诊断。     

燃油泵故障诊断试验装置及试验方案优化设计

针对现有燃油泵测试平台只能够完成简单的功能测试,在多故障模式条件下故障检测率低的问题,设计了一套燃油泵故障诊断试验装置及试验方案,并依据试验结果对方案进行了优化。该装置可针对燃油泵7种典型故障进行故障试验,并实时准确地采集其振动及出口压力信号。对采集到的信号进行故障特征提取,构造不同故障特征向量,比较不同传感器信号组合时的诊断效果,优化了传感器的布局。试验验证表明,该装置能够为燃油泵故障诊断提供可靠的故障数据,并且只需一个振动传感器和一个压力传感器就可以实现对泵的故障诊断,减小了工程应用中机载燃油泵状态监测系统的体积及复杂性。     

基于FNN-GA的车用传感器在线诊断

基于模糊神经网络与遗传算法,提出了柴油机传感器故障的模糊融合诊断策略;运用模糊推理算法,依据不同故障的传感器波形信号,对传感器故障模式进行了判别,验证了故障诊断网络的可靠性。利用柴油机电控平台,进行了柴油机MAP、RPS和TPS的硬故障和软故障等性能试验。结果表明:所设计的传感器故障诊断模型合理,诊断策略具有较好的识别率,可用于柴油机传感器故障在线诊断。     

基于噪声信号小波包分析的动力设备故障智能诊断

以柴油机为代表的动力设备噪声信号是一种典型的非平稳信号。为了实现动力设备精确故障诊断,针对噪声信号引入一种非平稳信号分析方法——小波包分析。首先利用声音传感器采集柴油机不同工作状态下的噪声信号;然后根据柴油机噪声信号的特点,选择DB4小波基对信号进行3层小波包分解,将信号的频段分解为8个子频段,以每个子频段的能量信息作为特征向量;最后使用K-近邻法对特征向量进行判别,从而实现故障诊断。实验结果表明,该方法有着较高的故障诊断率,可以实现动力设备的故障智能诊断。     

轴承状态的声信息流识别法

根据噪声信息流的传播途径研制了固体声传感器和检测系统,并采用全息频谱法分析处理固体声信息,以确定轴承的运行状态。实验结果表明,将此方法用于轴承故障识别时,能确切提取反映轴承工作状态的信息流。     

AlN超声换能器声场的仿真分析

超声换能器声场性能是换能器优劣的一个重要指标,对声场中的一系列理论进行了分析推导,分别对换能器声场进行频域和瞬态分析。频域分析中研究了声压、声压级随频率变化规律、油液区域中声场的分布、衰减的问题。瞬态分析中展示了声波在油液域的传播过程,对比了声信号在换能器两侧的传播,分析声波在各个反射界面的强度变化。结果与理论分析一致性较好,为后续利用设计制作换能器提供一定的依据。     

FAULT DIAGNOSIS OF ROTATING MACHINERY THROUGH VISUALISATION OF SOUND SIGNALS

As a method for diagnosing faults in rotating machinery, attention is being focused on changes in the sound signals generated by bearings. This provides the advantage of making it easier to set up sensors, since sound signals can be measured at a location some distance from the housing of the bearing. However, the signal-to-noise ratio is low compared with the vibration acceleration, which makes it difficult to identify any characteristic difference between the sound signals generated by normal and faulty bearings. This report describes a symmetrised dot pattern (SDP) method, which visualises sound signals in a diagrammatic representation. Using SDP to visualize sound signals measured for fans, it was possible to distinguish differences between normal and faulty bearings. Moreover, through the analysis of sound signals in the time-frequency domain and wavelet analysis, the signal component indicative of a fault was identified. When sound signals were modified by removing the above component, SDP with the modified faulty signal resembled the non-faulty case.     

Thermal analysis MLP neural network based fault diagnosis on worm gears

The importance of fault diagnoses, in any kind of machinery, can’t be over stated. Any undetected small fault in machinery will most probably rise with time and will cause machinery to shut down thus resulting in both mechanical and more importantly economical loss for the industry. In recent years, researches have been done for the faults diagnosis through the analysis of their vibration and sound signatures. The extraction of those characteristic signatures is a complicated process because complexities in modern day machineries can results in many vibration and sound generating sources. This paper presents a condition based fault diagnoses technique to detect the condition of gear. An experimental setup, consisting of a worm gear driven by an electric motor, was setup to conduct tests under different working conditions. The vibration and sound signature signals of worm gear were examined for normal and faulty conditions under different speeds and oil levels. The collected data was then used for feature extraction, by using Fast Fourier Transform to filter background noise signals and to collect only the signature of the gearbox vibration and sound signals. An MLP (Multilayer Perceptron) Artificial Neural Network Model has been developed to classify the signature signals. A thermal camera is also used to observe the heating patterns for all those working conditions. With the help of MLP Artificial Neural Network it is possible to predict the speed and oil level of the gearbox and hence a possible fault diagnoses is also feasible.     

基于BP神经网络的金属裂纹声发射信号特征参数的提取

金属裂纹声发射信号特征提取是根据其进行故障诊断的关键,提出了BP神经网络和模式识别相结合的提取金属材料疲劳声发射信号特征的新方法,并利用美国PAC公司SAMOS声发射检测系统采集到声发射的各种参数,应用该方法选择出一些对分类识别最有效的特征参数;并采用可分离性判据进一步验证其正确性。     

利用声场空间分布特征诊断滚动轴承故障

基于振动信号分析的特征提取是目前最主要的机械故障诊断方法,而振动信号的获取受到接触式测量的限制,基于声学测量的故障诊断能够克服这一缺点,但传统基于单通道测试的声学诊断技术存在测点选择难和局部诊断的不足。基于近场声全息技术提出一种用于滚动轴承故障诊断的声场分布特征提取方法。不同轴承故障能产生不同的振动特性,进而产生相应的声场分布,鉴于轴承状态与声场分布特性的对应关系,利用近场声全息算法重建声源附近各轴承运行状态下的声场,得到反映声场分布的二维声像图,再从声像图中提取故障相关的灰度共生矩阵特征,建立声场分布特性与轴承运行状态间的内在联系,结合支持矢量机模式分类,用于轴承的故障诊断。研究表明所提出的声场分布特征提取方法能够有效地用于滚动轴承的各类故障诊断,为机械故障诊断提供了新的参考。     

基于声全息的故障诊断方法

基于振动信号的故障诊断方法在某些场合下存在着局限性.机械噪声蕴含着丰富的设备状态信息,而且具有非接触式测量的优点,可以部分地替代振动信号,用于故障诊断.传统的噪声诊断方法主要基于频谱分析,无法反映声源位置和强度 /的变化信息,只能进行初步的故障诊断.基于此,提出一种基于声全息的故障诊断方法.该方法采用由少量传声器组成的阵列测量声压,应用波叠加法重构物体的外部声场,可以方便快速地进行声场可视化.一旦准确地重建出物体的外部声场,就可以利用这些全息场的信息进行故障诊断.通过建立基于全息图的正常状态与故障状态的模板,将机器的运行信息与这些模板对比,就可以判定机器的运行状态,从而进行故障诊断.采用由多个脉动球组成的声源模型进行了数值仿真,并在消声室内对两只音箱噪声源进行了试验研究,都准确地识别出辐射体声场状态变化,找出了故障.从而验证了该方法的正确性和实用性,为其在现场应用打下基础.     

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 .     

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.