Source location of acoustic emission in diesel engines

This paper is the third in a series developing methods of mapping acoustic emission (AE) signals and wave propagation in engines and focuses on source location techniques for the multi-source signals on relatively complex structures typical of machinery applications. Two source location techniques, a traditional wave velocity-based and an AE energy-based technique, using triangular sensor arrays, are used to locate source positions on the cylinder head of a 74 kW diesel engine using simulated sources (pencil lead break) and real sources (e.g. injectors (INJs) and exhaust valves during engine running).Source location using both techniques is demonstrated on the cylinder head of a 74 kW four-stroke diesel engine. The velocity-based technique uses AE wave speeds and time-of-flight (wave arrival time) to locate source position and is found to be most effective for single source signals with a sharp rising edge and good signal to noise ratios. The energy-based technique is based on a simple absorption attenuation model and was found to be useful for multiple source signals such as INJ signals, although structure-specific attenuation coefficients need to be measured for accurate source location.     

Monitoring tool wear during wood machining with acoustic emission

The purpose of this study was to determine the degree of change in acoustic emission (AE) during cutting as a cutter tool was worn. AE is defined as the stress or pressure waves generated during dynamic processes in materials and is generated during fracture, delamination, deformation and distortion of wood during cutting. Previous work has shown that AE is sensitive to changes in the chip formation process and therefore could be used to monitor continuously the state of the cutting process. For this study a single-point cutting tool was worn by turning a green-white fir (Abies concolor (Gord. and Glend.) Lindl.) log on an engine lathe equipped with an automatic feed. The relationship between the AE output and the amount of wood cut was close to linear in the initial stages of the blade wear. As the blade became severely worn, the AE levels dropped dramatically and an asymptotic relationship between the two variables became evident.     

Surface anisotropy for monitoring the wear of IC engine cylinders

The present work proposes a methodology for monitoring the wear of internal combustion engine cylinders. This could be useful in calculating the time at which maintenance should be done on such a tribosystem or in determining its lifetime. On the basis of experimental data obtained using a friction—wear simulator, and through the determination of certain standardised and non‐standardised micro‐geometric parameters of the cylinder surface that change with respect to operating time, it is shown that the characteristic that exhibits the most variation is the cylinder surface anisotropy. This factor participates to a large degree in the partial hydrodynamic lubrication mechanism of the reciprocating piston ring system and, as it is associated with the stage of wear, better expresses the characteristics of the cylinder's worn surface. As a consequence, by prescribing a limit for the maximum acceptable anisotropy as a basic criterion, the ultimate tolerable stage of wear is controlled simultaneously and vice versa.     

车削过程异常智能化声发射(AE)监视系统的研究

本文阐述了一种车削过程异常智能化监视系统的。作原理。根据这一原理建立的专家系统型车削过程异常声发射监视装置，有效地提高了识别成功率，降低了误报率。     

The milling tool wear monitoring using the acoustic spectrum

In the present study, the tool wear has been monitored using the cutting sound acoustic spectrum and the linear predictive cepstrum coefficient (LPCC) of the milling sound signal would be extracted to be used as the acoustic spectrum characteristic parameters. The relationship between each order component of LPCC and the flank wear of the tools was analysed. The experimental results show that there are clear characteristic components in the milling sound signal related to the tool wear. It has been found that the characteristic components associated with tool wear are mainly concentrated in the sixth-, seventh- and eighth-order components of LPCC.     

An investigation of acoustic emission in sliding friction and wear of metals

Measurements of acoustic emission (AE) in severe sliding of metallic specimens were performed with a view to determining relationships, if any, between AE and wear-friction parameters. It was found that AE is readily observed in dry sliding and that emission rates and cumulative count data are sensitive to the external variables which influence tribological contact conditions.

A relationship between cumulative AE count and frictional work is proposed and possible reasons for the form of the relationship are discussed. Emissions did not appear to be directly dependent on rates of wear but the possibility that AE-wear correlations exist should not be precluded in view of recent work in other laboratories using significantly different apparatus and instruments.     

Corrosive wear in crosshead diesel engines

Existing theories describe corrosive wear in crosshead diesel engines in terms of acid condensation, when the temperature in the combustion space drops below the dew point. Here a theory is proposed which stresses the role of alkaline cylinder lubricants in protecting the metal surface. Calculated liner wear rates as a function of fuel sulphur content, feed rate and alkalinity of the oil, and axial position are in satisfactory agreement with experimental data.     

环形腔全光纤F-P干涉仪的声发射检测实验研究

提出了一种新型结构的用于声发射检测的全光纤F-P干涉仪。选用2×2光纤耦合器,将耦合器的一个入射端与一个出射端焊接相连,以耦合器代替传统的反射腔面,构成光纤环形传输腔,腔体贴附或埋入待测固体中检测声发射信号。通过理论推导和计算机仿真,确定了此结构光纤传感器的检测特性。实验以大理石板作为待测介质,对利用信号发生器驱动PZT（压电陶瓷）作为已知超声源在大理石板中产生的连续型声发射信号,及冲击波作用下大理石板中产生的突发型声发射信号进行了检测,并利用Fourier变换,得到了声发射信号的特征频率。实验结果表明,此种结构传感器能够检测材料结构中促使光纤轴向伸缩长度的量级为10-8m的声发射信号并识别其特征频率,该结构光纤传感器无需光程的匹配,适用于大尺度构件的监测,为材料结构健康检测与监控提供了一种新的方法。     

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.     

A study of tool wear using statistical analysis of metal-cutting acoustic emission

Many aspects of the interactions between cutting tools, workpiece material and the chips formed during machining that affect the wear and failure of the tool are not fully understood. The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and tool wear and as a methodology for detecting tool wear and failure on line. A brief review of the theory of acoustic emission is presented. Acoustic emission data from reduced contact length machining experiments and tool flank wear tests are analyzed using distribution moments. The analysis shows that the skew and kurtosis of an assumed β distribution for the r.m.s. acoustic emission signal are sensitive to both the stick-slip transition for chip contact along the tool rake face and progressive tool wear on the flank of the cutting tool.     

Identification of tool wear using acoustic emission signal and machine learning methods

The work concerns the monitoring of the edge condition based on acoustic emission (AE) signals. The tool edge condition was determined by the wear width on the flank face. The processed material was an aluminum-ceramic composite containing 10% SiC. A carbide milling cutter with a diamond coating was used as the tool. Based on the AE signals, appropriate measures were developed that were correlated with the edge condition. Machine learning methods were used to assess the milling cutter's degree of wear based on AE signals. The applied approach using a decision tree allowed the prediction error of the tool condition class with a value below 6%. The method was also compared with other machine learning methods such as neural networks and the k-nearest neighbor algorithm.     

Statistical process control of acoustic emission for cutting tool monitoring

The problem of cutting process monitoring has been investigated in recent years, with encouraging results, using pattern recognition analysis of acoustic emission (AE) signals. The analyses are based on linear discriminant functions, which assume that the observed data (from each class) are independent random samples from multivariate normal distributions with equal covariance matrices. However, in a number of practical situations some (or all) of these assumptions may not necessarily hold, resulting in errors in the analysis.In this paper, the distributions of AE spectra generated in earlier work are first analysed, and the results indicate departure from the assumptions, although the lack of normality was not too severe. Relaxing the assumption of equality of the covariance matrices, quadratic discriminant function analysis produced improved results for tool wear and chip noise monitoring while degrading tool fracture detection. The latter is due to inadequacy of the amount of data used in training the system. It is expected that increasing the data base would improve the results for all classes.The analysis until now has focused on reducing the dimensionality of the feature space by eliminating the features with the least discriminatory power. Even though this inevitably reduces the performance of the system, it is a necessary compromise for increased computational speed. To make use of the entire feature set with a reduced matrix rank, a principal component analysis is investigated. The result is a substantial improvement in correct classification of AE signals, even under different cuting conditions.     

基于声发射技术飞机关键部件健康监测方法

为解决飞机关键结构部件疲劳损伤的有效监测,及时发现潜在的安全隐患,避免灾难性事故的发生.对于采用先进声发射技术所监测到的某飞机水平尾翼的原始声发射信息,提出采用小波包分析与支持向量机相结合的方法对匕机水平尾翼的健康状况进行识别与诊断.该方法将飞机水平尾翼产乍的原始声发射信号进行多级小波包分解,提取其频带能量作为特征向量,输入到由支持向量机构建的健康监测器对其进行健康识别与诊断.实验结果表明,该方法可以有效、准确地识别并诊断出飞机水平尾翼的疲劳裂纹,为飞机结构部件健康状态的有效监测提供了新途径.     

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.     

Predictions of cam follower wear in diesel engines

The rate of wear of cam followers in a valve train system is mainly a function of contact stress between the cam and the follower, sliding velocity and hydrodynamic film thickness between the two mating surfaces. The problem of surface fatigue wear becomes severe as the contact between cam and follower exceeds the plasticity limit of material. It finally leads to an increase in valve lash and loss of engine performance. The wear is minimised by reducing the coefficient of friction and by minimizing the compressive stress.In this paper, an attempt has been made to estimate the wear of followers quantitatively. The profile of followers resulting from steady and non-catastropic wear processes is computed by combining a linear wear relation and an elasto-hydrodynamic or boundary lubrication transition model with kinematic analysis. The finite element analysis, AVL TYCON simulation program and classical methodology have been effectively used to predict the follower wear. The model was validated on all types of followers widely varying in size, brake mean effective pressure and speed. The predicted wear profiles exhibit satisfactory agreement with experimental observations. At the end of the paper, a design guideline for designing a cam follower for low wear rate is given.     

声发射智能传感器远程监测网络系统的研究

提出了一种新颖的声发射智能传感器网络系统。该系统为三层模块化结构。宽带声发射（AE）传感器采集被测对象的声发射信号;利用DSP实时提取结构缺陷的声发射特征参数并传送给智能终端;最终以无线／有线方式向用户传送有效信息。经实际应用证明该系统具有实时在线远程无损监测大桥。大坝,隧道等钢筋混凝土结构工程健康状况的功能。且系统采用了GPRS和互联网,降低了系统复杂性和成本。     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

基于噪声信号的难加工表面损伤在线监测

提高难加工材料的加工效率在机械加工中有着巨大的经济价值,但是由于一些加工表面损伤的存在如白层,难加工材料的加工效率仍处于一个很低的状态.加工材料表面的白层只能在加工之后才能被发现.这对刀具磨损,加工质量等有着巨大的危害.因此在线监测难加工表面损伤具有巨大的经济意义.建立一个基于实时切削噪声信号的在线监测系统对白层,表面质量和刀具磨损进行监测分析.实验表明噪声信号均方根值,频率与白层有着极大的相关性.     

An investigation of acoustic emission to monitoring flat lapping with non-replenished slurry

Lapping is a precision manufacturing process. However, the material removal rate and surface roughness show significant variation between trials for repeated experiments and, thus, the repeatability of the results depends on the machine operator’s skill. Acoustic emission (AE) seems to be capable of monitoring the process. Therefore, an understanding of AE generation during lapping is important to predict the performance of the grains and hence the lapping results. Based on a theoretical analysis and experimental results collected during flat lapping, the AE signal was investigated for the situation when slurry is supplied without replenishment. The experiments were carried out with a wireless rotating AE sensor mounted in the middle of the lapping plate. Three parameters related to the AE curve are proposed to monitor the process. The influence of process parameters (lapping pressure, velocity, average grain size, concentration of grains in lapping compound and the number of conditioning rings) on the characteristics of the AE curve was investigated.