滚动轴承声信号特征提取和诊断试验研究

为解决振动检测方法不能有效识别低速旋转机械滚动轴承故障问题,利用声发射检测方法,建立了滚动轴承低速声发射信号采集试验装置,对模拟人工缺陷滚动轴承声发射信号进行了采集,进而对滚动轴承声发射信号进行总体平均经验模式分解,结合能量矩及相关系数法综合判断分解后各模态分量的真伪,据此提取出特征信号并做出其局部Hilbert边际谱,最后对滚动轴承各种故障模式进行诊断。试验结果表明该诊断方法能准确识别滚动轴承声发射信号故障频率,依据特征频率及幅值大小可对低速滚动轴承故障进行有效诊断。     

Low speed bearing fault diagnosis using acoustic emission sensors

In this paper, a new methodology for low speed bearing fault diagnosis is presented. This acoustic emission (AE) based technique starts with a heterodyne frequency reduction approach that samples AE signals at a rate comparable to vibration centered methodologies. Then, the sampled AE signal is time synchronously resampled to account for possible fluctuations in shaft speed and bearing slippage. The resampling approach is able to segment the AE signal according to shaft crossing times such that an even number of data points are available to compute a single spectral average which is used to extract features and evaluate numerous condition indicators (CIs) for bearing fault diagnosis. Unlike existing averaging based noise reduction approaches that require the computation of multiple averages for each bearing fault type, the presented approach computes only one average for all bearing fault types. The presented technique is validated using the AE signals of seeded fault steel bearings on a bearing test rig. The results in this paper have shown that the low sampled AE signals in combination with the presented approach can be utilized to effectively extract condition indicators to diagnose all four bearing fault types at multiple low shaft speeds below 10 Hz.     

Analysis on accuracy improvement of rotor–stator rubbing localization based on acoustic emission beamforming method

This paper attempts to introduce an improved acoustic emission (AE) beamforming method to localize rotor–stator rubbing fault in rotating machinery. To investigate the propagation characteristics of acoustic emission signals in casing shell plate of rotating machinery, the plate wave theory is used in a thin plate. A simulation is conducted and its result shows the localization accuracy of beamforming depends on multi-mode, dispersion, velocity and array dimension. In order to reduce the effect of propagation characteristics on the source localization, an AE signal pre-process method is introduced by combining plate wave theory and wavelet packet transform. And the revised localization velocity to reduce effect of array size is presented. The accuracy of rubbing localization based on beamforming and the improved method of present paper are compared by the rubbing test carried on a test table of rotating machinery. The results indicate that the improved method can localize rub fault effectively.     

Effects of operating conditions on the Acoustic Emissions (AE) from planetary gearboxes

In the last decade, the use of acoustic emissions has received growing acceptance for its application in machine condition monitoring. This is because it offers good possibilities to diagnose failures at early stages and low rotational speeds. The use of acoustic emissions for condition monitoring of gears, however, is still an active field of research, because several questions remain unanswered. One of these questions is the effect of operating conditions on the AE generated during gear meshing. In this work, the results of experiments carried out on a non-faulty planetary gearbox test bench are presented. A planetary gearbox is considered, because of its usual application on machines subjected to variable operating conditions. The effects of lubricant temperature, load and rotational speed are investigated. The conclusions obtained from the experiments are used for the analysis of the AE measured on the planetary gearbox of a bucket wheel excavator.     

A fault diagnosis scheme of rolling element bearing based on near-field acoustic holography and gray level co-occurrence matrix

Vibration signal analysis is the most widely used technique in condition monitoring or fault diagnosis, whereas in some cases vibration-based diagnosis is restrained because of its contact measurement. Acoustic-based diagnosis (ABD) with non-contact measurement has received little attention, although sound field may contain abundant information related to fault pattern. A new scheme of ABD for rolling element bearing fault diagnosis based on near-field acoustic holography (NAH) and gray level co-occurrence matrix (GLCM) is presented in this paper. It focuses on applying the distribution information of sound field to bearing fault diagnosis. A series of rolling element bearings with different types of fault are experimentally studied. Sound fields and corresponding acoustic images in different bearing conditions are obtained by fast Fourier transform (FFT) based NAH. GLCM features are extracted for capturing fault pattern information underlying sound fields. The optimal feature subset selected by improved F-score is fed into multi-class support vector machine (SVM) for fault pattern identification. The feasibility and effectiveness of our proposed scheme is demonstrated on the good experimental results and the comparison with the traditional ABD method. Considering test cost, the quantized level and the number of GLCM features for each characteristic frequency is suggested to be 4 and 32, respectively, with the satisfactory accuracy rate 97.5%.     

Seeded fault detection on helical gears with acoustic emission

Acoustic emission (AE) is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of AE in monitoring gearboxes this has been solely applied to spur gears. This report presents an experimental investigation that assesses the effectiveness of AE in identifying seeded defects on helical gears; the first known attempt. Additionally vibration analysis has performed to study the effect of seeded defect on the vibration signature of the meshing gears.     

Condition monitoring of worm gears

Condition monitoring of key components in rotating machines such as gearboxes ensure reduction in costly unscheduled machine down time. This paper explores the possibility of monitoring seeded defects on worm gears with acoustic emission, and, comparisons are made with vibration analysis. Unlike other types of gearboxes, monitoring of worm gearboxes is not widely documented. It is shown that AE offers better diagnostic capability than vibration analysis.     

金刚石单晶生长的声发射实时检测初探

采用声发射检测技术,对高温高压下人造金刚石单晶的生长过程进行了检测和分析。利用由PCI-8型声发射仪和LMD-800型铰链式六面顶压机组成的声发射检测系统,检测了金刚石单晶的生长过程。将金刚石单晶生长和不生长过程中检测到的声发射信号进行对比和频谱分析,结果表明:声发射信号与金刚石单晶生长过程存在对应关系;金刚石单晶生长对应的声发射信号是一种低频信号,可以利用声发射信号的变化规律研究金刚石单晶在高温高压条件下的原位反应机理。     

Acoustic-based damage detection method

Most of the structural health monitoring (SHM) methods is either based on vibration-based and contact acoustic emission (AE) techniques. Both vibration-based and acoustic emission techniques require attaching transducers to structure. In many applications, such as those involving hot structural materials for thermal protection purposes or in rotating machines, non-contact measurements would be preferred because the operating environment is prohibitive leading to potential damage in contact sensors or their attachments. In this paper, a new non-contact, acoustic-based damage detection method is proposed and tested with an objective that the proposed method is able to detect the location and extend of damage accurately. The proposed acoustic-based damage detection method is a direct method. In this proposed method, changes in vibro-acoustics flexibility matrices of the damage and health structure are used to predict the location and extend of damage in the structure. A case study involving actual measured date for the case of a fixed–fixed plate structure is used to evaluate the effectiveness of the proposed method. The results have shown that the proposed acoustic-based damage detection method can be used to detect the location and extend of the damage accurately.     

Acoustic emission during the development of a Lüders band in a low-carbon steel

The acoustic emission (AE) during the plastic deformation of a low-carbon steel is experimentally studied. The data obtained on the time localization of AE signals in a sample allow the motion of a Lüders band to be traced. The main informative AE parameters at various stages of plastic flow are analyzed. The results are compared with speckle video filming data and are supported by them. The possibilities of determining plastic deformation stages and Lüders band characteristics using acoustic emission are discussed.