Influence of operational variables in a hydrodynamic bearing on the generation of acoustic emission

Detecting mechanical faults of rotating machines particularly in hydrodynamic bearings has been recognised as important for preventing sudden shut downs. This technical note presents an experimental investigation that is aimed at understanding the influence of operational variables (speed, load, etc) on generation of acoustic emission in a hydrodynamic bearing. It is concluded that the power losses of the bearing are directly correlated with acoustic emission levels.     

Monitoring of through-feed centreless grinding processes with acoustic emission signals

Grinding is a mechanical removal process applied mainly in finishing operations of hardened workpieces to produce small tolerances with high-quality. Especially, centreless grinding is broadly used in serial production due to the requirement of high accuracy in process. Centreless grinding is used to produce several mechanical components such as, bushings, needles, ball bearings, valves, and stems for shock absorbers. However, the setup of machine tools is very complex and needs long time due to the great number of input variables that should be checked and configured. The acoustic emission monitoring can be used to help the first setup or during the grinding process becoming a on-line detection system. Considering the importance of obtaining an efficient methodology to predict and detect the surface quality and the dimensional errors, a monitoring of the frequency on the spectrum of acoustic emission (EA) was conducted, related to surface roughness Rz, cylindricity, and roundness. The FFT and Wavelet were applied aiming to help the analysis of data and provide the best understanding of the signal and generating an intelligent information in the automation in grinding process. Thus, in this work the results showed that the analysis of the harmonic content of acoustic emission signal is a powerful tool to monitoring the centreless grinding process.     

BK7光学玻璃金刚石划刻声发射信号的特征提取

以金刚石压头划刻BK7光学玻璃为研究对象,分析了脆性材料脆性去除过程中的声发射机制,研究了声发射信号的特征提取技术。多种切深实验显示:BK7光学玻璃发生脆性去除的特征主要集中在[100,200]kHz、[300,400]kHz两个频段,对应不同的声发射机制,其中[100,200]kHz频带的滤波信号呈现明显的、时间间歇的突发式声发射现象,与脆性材料裂纹的生成与扩展密切相关。基于上述实验结果,提出了以突发式声发射事件为单位的特征监测方法。针对该带通滤波信号的均方根值(RMS),研究了基于凸优化理论的声发射事件识别算法,得到了脆性材料裂纹扩展的时刻及能量信息。得到的结果表明:以声发射事件为单位的特征监测具有明确的物理意义,能够更加客观地表征脆性材料的去除过程。     

Characterization of nugget nucleation quality based on the structure-borne acoustic emission signals detected during resistance spot welding process

Acoustic emission signals detected during the resistance spot welding of aluminum alloy were studied in order to assess the characterizations of welding process, the characterizations of the effect of welding parameters to nugget nucleation and the characterizations of the nugget quality by the analysis to acoustic emission signals. The results showed that the physical phases of nugget nucleation can be characterized by the acoustic emission signals detected during the resistance spot welding process. The effects of welding current and current duration to nugget nucleation can be characterized by the characteristic parameters of acoustic emission signals. The characteristic parameters of acoustic emission signals had a better relevance to nugget dimensions and weld strength, which made it possible to measure or predict the weld strength by the characteristic parameters of acoustic emission signals detected during the resistance spot welding process.     

正交磁场对电火花单脉冲放电间隙的影响

通过普通电火花单脉冲放电（EDM）与磁场辅助电火花单脉冲放电（MF-EDM）放电间隙对比试验,分析得出不同磁感应强度、开路电压、放电电容及电极外伸端长度对磁场辅助电火花加工放电间隙的影响规律。试验发现：随着磁感应强度的增大,放电间隙增大;开路电压和放电电容对放电间隙的影响较小;随着电极外伸长度的增大,放电间隙变小。最后对试验数据进行部分析因分析,得出磁场辅助电火花加工中影响放电间隙的主要因素为磁感应强度、电极外伸长度及其交互耦合作用。     

Evaluation of tribological properties of bearing materials for marine diesel engines utilizing acoustic emission technique

Acoustic Emission (AE) technique, which has detection capability for minute failures, has been tried to monitor the condition of a plain bearing under the laboratory conditions. In this paper, the bearing materials for marine diesel engines - tin alloy as known as “white metal”, aluminum alloy of 40% tin mass and aluminum alloy 40% tin mass with resin overlay - were tested using a sleeve-to-plate tribo-tester. The frictional force and back temperature were measured as well as the AE signals. The possibility of AE technique to monitor the bearing condition was also assessed by evaluating tribological properties under different operating conditions such as start-stop simulating the crankshaft turning during engine assembly and seizure tests. These results indicate that AE is useful for monitoring the lubricated condition of the sliding surfaces and evaluating tribological properties of the bearing.     

Smart portable noninvasive instrument for detection of internal air leakage of a valve using acoustic emission signals

Acoustic emission (AE) can be used to detect and determine the internal leakage rate through a valve in many applications. However, a general AE data acquisition system is expensive and bulky. This paper presents a novel low-cost instrument based on microcontroller and a novel theoretical model based on AE technique to predict the leakage rate. The system is an embedded system instead of a general PC-based data acquisition. AE_RMS parameter is used to infer the leakage rate, and the effects of various process variables on the model are also studied. The experimental results have shown that the instrument is capable of detecting possible valve leakage encountered in online operation. With its portability, ease of use and compactness, the proposed system provides faster and low cost valve leakage detection.     

Incipient damage detection and its propagation monitoring of rolling contact fatigue by acoustic emission

The acoustic emission (AE) technique was applied to rolling contact fatigue tests using a test-rig running under constant load and speed for detecting the incipient damage and damage location. This incipiently-damaged roller was investigated in detail and monitored by further running to determine the damage severity and to understand the surface damage propagation process by applying the AE techniques. The conventional AE parameters and AE signal features were studied, and their relation with the AE source locator hit count rate were correlated. The results demonstrated the successful use of the AE measurement unit, which is principally, consists of the AE data analyzer and the AE source locator as a new system for detecting incipient damage produced by fatigue. Moreover, the system is able to forecast the position of the damage in the roller, capable of providing an indication of the severity of damage i.e. damage size, and thus it could allow the user to monitor the rate of further degradation of the rolling elements.     

A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Tool condition monitoring, which is very important in machining, has improved over the past 20 years. Several process variables that are active in the cutting region, such as cutting forces, vibrations, acoustic emission (AE), noise, temperature, and surface finish, are influenced by the state of the cutting tool and the conditions of the material removal process. However, controlling these process variables to ensure adequate responses, particularly on an individual basis, is a highly complex task. The combination of AE and cutting power signals serves to indicate the improved response. In this study, a new parameter based on AE signal energy (frequency range between 100 and 300 kHz) was introduced to improve response. Tool wear in end milling was measured in each step, based on cutting power and AE signals. The wear conditions were then classified as good or bad, the signal parameters were extracted, and the probabilistic neural network was applied. The mean and skewness of cutting power and the root mean square of the power spectral density of AE showed sensitivity and were applied with about 91% accuracy. The combination of cutting power and AE with the signal energy parameter can definitely be applied in a tool wear-monitoring system.     

Application of acoustic emission technique for the detection of defects in rolling element bearings

The usefulness of acoustic emission (AE) measurements for the detection of defects in roller bearings has been investigated in the present study. Defects were simulated in the roller and inner race of the bearings by the spark erosion method. AE of bearings without defect and with defects of different sizes has been measured. For small defect sizes, ringdown counts of AE signal has been found to be a very good parameter for the detection of defects both in the inner race and roller of the bearings tested. However, the counts stopped increasing after a certain defect size. Distributions of events by ringdown counts and peak amplitudes are also found to be good indicators of bearing defect detection. With a defect on a bearing element, the distributions of events tend to be over a wider range of peak amplitudes and counts.     

磨削过程知识学习

磨削加工光磨时间、加工节拍直接体现磨削加工参数,粗磨过程的好坏是磨削过程的关键,磨削过程声发射信号粗磨段上升部分包含着磨削过程最丰富的信息,采用平均三角分配模糊规则对磨削过程声发射信号粗磨段上升部分进行知识获取和自学习,建立磨削加工光磨时间、加工节拍与磨削声发射曲线粗磨段上升部分斜率之间的对应关系,据此可得到任意光磨时间、加工节拍时对应的磨削声发射曲线粗磨段上升部分斜率。以此判断加工参数选择的合理性,以实现磨削加工的加工参数自动选择和智能控制,确保加工质量,实现磨削过程加工参数在线调整、磨削智能化。     

The influence of operating condition on acoustic emission (AE) generation during meshing of helical and spur gear

The application of high-frequency acoustic emission (AE) technology to condition monitoring of gears is still in its infancy. Understanding the influence of gear operating parameters on the generation of AE is essential in applying the AE technology to gear condition monitoring. This paper presents experimental findings on the influence of speed and load in generating AE for operating helical and spur gears. The experimental findings suggest that any percentage reduction in specific film thickness (λ), a direct consequence of a change in load condition, results in a nine- and four-fold percentage change in AE rms for the spur and helical gear sets, respectively. A numerical model representing changes in AE rms with variation in load and speed under near isothermal conditions for spur and helical gears was also established. In conclusion, it is postulated that the AE technology could offer a means of measuring in situ the effectiveness of a lubricant for operational spur and helical gears thereby establishing if the correct lubricating conditions are present to ensure optimal life usage.     

Study on the thermo-effect of P-GMAW characterized by structure-borne acoustic emission signals detected in welding on aluminum alloy

According to the structure-borne acoustic emission (AE) signals detected in pulsed gas metal arc welding (P-GMAW), the effects of welding heat input and pulses to the microstructures and the characteristics of AE signals were analyzed. The experiment results showed that the welding arc was the source of most vibration energy in GMAW. The increase of welding heat input caused the growth of grain in weld and the increase of average AE count. The mean grain size was increased with the average AE count increasing, which was attributable to the welding heat input. Both the welding arc and the pulses used in welding were the source of vibration energy in P-GMAW. The mean grain size decreased with the average AE count increasing, which was attributable to the pulses provide additional vibration energy to refine the grain structure. The welding heat input effect played a more important role than the pulses effect to the grain structure in P-GMAW.     

砂轮磨损状态的声发射检测及其误差补偿方法的研究

砂轮磨损会破坏砂轮型面,对加工精度有重要影响。利用声发射方法检测砂轮的磨损状态以及修整过程,并提出了实现砂轮磨损误差在线补偿的方法。     

混粉电火花加工中极性效应的研究

为研究极性效应对混粉电火花加工的影响规律．采用钢对钢加工、铜对钢加工两种电极组合在添加硅粉的煤油工作液及普通煤油工作液中进行实验，并更换不同的极性，考察了两极材料的去除率和表面粗糙度，结果表明负极总能得到更高的材料去除率，而正极总能得到更低的表面粗糙度值。此现象可从两极表面能量密度差异的角度予以解释。     

A novel measurement method for transient detection based in wavelets entropy and the spectral kurtosis: An application to vibrations and acoustic emission signals from termite activity

This paper presents a novel non-destructive method for termite detection that uses the entropy of the continuous wavelet transform of the acoustic emission signals as an uncertainty measurement, to achieve selective frequency separation in complex impulsive-like noisy scenarios, with the aid of the spectral kurtosis as a validating tool. The goal consists of detecting relevant frequencies, by looking up the minima in the curve associated to the entropy of the difference between the raw data and the wavelet-based reconstructed version. By measuring the signal’s uncertainty, the scales corresponding to the entropy minima, or pseudo-frequencies, manage to target three main types of emissions generated by termites: the modulating components (enveloping curve), the carrier signals (activity, feeding and excavating), and the communicating impulses bursts (alarms). The spectral kurtosis corroborates the location of the entropy minima (optimum uncertainty) matching them to its maxima, associated to frequencies with the highest amplitude variability, and consequently minimizing the measurement uncertainty. The method is primarily conceived to cover the acoustic-range, in order to acquire signals via standard sound cards; a broaden high-frequency study is developed for the assessment, and with the added value of discovering new and higher frequency components of the species emissions. The potential of the method makes it useful for myriads of applications in the frame of nondestructive transient detection.     

混粉电火花加工中粉末对工件表面的影响

对不同加工条件下混粉电火花加工后工件表面的硅含量进行了对比测量。实验结果表明：当峰值电流小于4A时，混粉电火花加工后的工件表面硅含量随峰值电流的增大而急剧减小，而当峰值电流大于4A时，工件表面硅含量随峰值电流的增大而缓慢增加；混粉电火花加工后的工件表面硅含量随脉宽的增大而增加；在其他加工条件相同的情况下，对于相同的单次放电脉冲能量，混粉电火花加工获得的工件表面硅含量随峰值电流变化的关系呈近似二次曲线。引入熵的概念，对产生上述结论的原因进行了分析，并解释了混粉电火花加工可以改善工件表面质量的机理。     

Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage

The detection of acoustic emission (AE) signals produced by liquid and gas leakage through valves can be related directly to the qualitative leakage rate. This allows for cost estimation of losses in processes for several industries. However, to find out the relationship between qualitative leakage rate and AE signal large amounts of experimental data is needed. This paper presents a theoretical investigation of the acoustic emission to detect the internal leakage rate through a valve and experimental validation. The AE signals generated by internal liquid and gas leakage through valves were characterised. The effect of the influenced factors of leakage rates, inlet pressure levels, valve sizes and valve types, on AE parameter, AE_RMS, were studied and explained. The results of theoretical and experimental showed that AE signal power computed from the power spectral density (PSD) correlated well with influenced factors of leakage rates. Finally, a novel and inexpensive AE instrument has been invented for predicting qualitative leakage rate using a micro processor and derived relationship.     

基于声发射和神经网络的风机叶片裂纹识别研究

提出一种对风力机叶片裂纹声发射信号进行模式识别的方法。该方法以叶片无裂纹、萌生裂纹、扩展裂纹和断裂四个阶段为声发射源的四个模式,基于声发射信号含有丰富的发射源信息的特点,通过大量采样获得叶片裂纹声发射信号参数,并依照叶片裂纹声发射参数分析的数值特点确定BP神经网络,用选定的网络对叶片裂纹阶段进行模式识别,以判断裂纹的危害程度。仿真结果表明,利用BP神经网络可以对声发射信号进行有效识别,识别准确率达到90%以上。     

钢板声发射时间反转聚焦增强定位方法

声发射检测方法具有实时动态监测优点,应用越来越广泛,但是对声源的定位始终没有更大的突破。在钢板声发射检测中,提出一种基于时间反转理论的声发射源准确定位的方法。由于声发射检测是一种被动检测技术,结合时间反转聚焦理论,推导出对声源信号实现时间反转聚焦增强处理方法,可增强检测信号中声源幅值,提高信噪比;然后根据声源信号到达时间推算出声源聚焦时刻,利用弹性波传播理论对传感器监测区域重建信号传播波动图,显示出声源位置和区域;最后通过实验测试对该方法进行验证,结果表明该方法能有效提高损伤声源信号的能量,对检测区域的信号重建和定位显示准确地给出损伤声源位置。