Acoustic emission monitoring of high speed grinding of silicon nitride

Acoustic emission (AE) monitoring of a machining process offers real-time sensory input which could provide tool condition and part quality information that is critical to effective process control. However, the choice of sensor, its placement, and how to process the data and extract useful information are challenging application-specific questions which researchers must consider. Here we report an effort to resolve these questions for the case of high speed grinding of silicon nitride using an electroplated single-layered diamond wheel. A grinding experiment was conducted at a wheel speed of 149 m s-1 and continued until the end of the useful wheel life. AE signal data were then collected for each complete pass at given grinding times throughout the useful wheel life. We found that the amplitude of the AE signal monotonically increases with wheel wear, as do grinding forces and energy. Furthermore, the signal power contained in the AE signal proportionally increases with the associated grinding power, which suggests that the AE signal could provide quantitative information of wheel wear in high-speed grinding, and could also be used to determine when the grinding wheel needs replacement.     

Near-field beamforming analysis for acoustic emission source localization

This paper attempts to introduce a near-field acoustic emission (AE) beamforming method to estimate the AE source locations by using a small array of sensors closely placed in a local region. The propagation characteristics of AE signals are investigated based on guided wave theory to discuss the feasibility of using beamforming techniques in AE signal processing. To validate the effectiveness of the AE beamforming method, a series of pencil lead break tests at various regions of a thin steel plate are conducted. The potential of this method for engineering applications are explored through rotor-stator rubbing tests. The experimental results demonstrate that the proposed method can effectively determine the region where rubbing occurs. It is expected that the work of this paper may provide a helpful analysis tool for near-field AE source localization.     

铝与钢摩擦时声发射参数与正压强之间的关系

材料不同,声发射信号的特点不同。为了提高声发射技术在监测摩擦过程中的应用水平,研究不同材料的声发射信号的特点是必要的。本文研究了铝与45钢摩擦时产生的声发射信号的特点,实验研究结果表明:在铝与45钢的摩擦过程中,相对运动速度对于正压力与声发射信号之间的关系有较大的影响;低速下,正压力与声发射信号之间无规律可言;高速下,随着正压力的增加,声发射信号各参数是增加的。     

Energy Index technique for detection of Acoustic Emissions associated with incipient bearing failures

The application of Acoustic Emission (AE) technique to condition monitoring of gears and bearings is gaining significance as it can detect early symptoms of defects such as pitting, wear and flaking of surfaces. Such early detection of defects is of vital importance so as to avoid major failures with catastrophic consequences. This article presents results on the Energy Index (EI) technique, used in detecting masked AE signatures associated with the loss of mechanical integrity in bearings. Both simulated and real experimentally generated AE signatures were used to investigate the efficiency and applicability of the technique at signal-to-noise ratios as low as 0.25. In conclusion it is shown that the EI technique is effective in detecting AE burst buried in random noise thereby offering a complementary tool for the diagnostician.     

高强度钢手工焊接后过程的声发射实时监测研究

本针对实际工艺中使用的高强度钢，重点对手工焊接的冷却过程利用声发射技术进行实时监测。本次试验是和先前在不同型号的钢板在手工焊接过程中所产生的声发射信号进行比较的基础上进行的，主要目的是寻找高强度钢在手工焊接的冷却中所产生的活性裂纹在冷却阶段的声信号特征。     

A non-iterative partial discharge source location method for transformers employing acoustic emission techniques

Partial discharge (PD) causes premature insulation failure of transformers. It is essential to detect PD to avoid unwanted failure of transformers. Only detection of PD is not sufficient for a transformer of huge size, unless it is possible to locate. Acoustic partial discharge measurement is advantageous for PD source location. There are different algorithms for PD source location. These are iterative and require large number of acoustic emission (AE) sensors. This paper presents a non-iterative source location algorithm employing four AE sensors. This algorithm is applied to experimental data. Proposed algorithm is also applied to published data and compared with existing iterative methods. Main error for source location is due to arrival time calculation. In order to reduce the error in AE signal arrival time calculation, different arrival time calculation methods are discussed and a comprehensive method is proposed. By applying these methods, arrival time is calculated from measured signal.     

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.     

Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements

The condition monitoring of a lab-scale, single stage, gearbox using different non-destructive inspection methodologies and the processing of the acquired waveforms with advanced signal processing techniques is the aim of the present work. Acoustic emission (AE) and vibration measurements were utilized for this purpose. The experimental setup and the instrumentation of each monitoring methodology are presented in detail. Emphasis is given on the signal processing of the acquired vibration and acoustic emission signals in order to extract conventional as well as novel parameters-features of potential diagnostic value from the monitored waveforms. Innovative wavelet-based parameters-features are proposed utilizing the discrete wavelet transform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with the most interesting diagnostic behaviour are highlighted. The differences in the parameters evolution of each NDT technique are discussed and the superiority of AE over vibration recordings for the early diagnosis of natural wear in gear systems is concluded.     

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.     

Roller bearing acoustic signature extraction by wavelet packet transform,applications in fault detection and size estimation

Continuous online monitoring of rotating machines is necessary to assess real-time health conditions so as to enable early detection of operation problems and thus reduce the possibility of downtime. Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. In this paper the effect of defect size, operating speed, and loading conditions on statistical parameters of acoustic emission (AE) signals, using design of experiment method (DOE), have been investigated to select the most sensitive parameters for diagnosing incipient faults and defect growth on rolling element bearings. A modified and effective signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform (WPT) and envelope detection. Results show the superiority of the developed algorithm and its effectiveness in extracting bearing characteristic frequencies from the raw acoustic emission signals masked by background noise under different operating conditions. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double AE impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals.     

304不锈钢点蚀声发射监测研究

为研究304不锈钢在高于常温条件下的点蚀声发射特性,对70℃下6%氯化铁溶液中304不锈钢点蚀过程进行了声发射监测。采用参数和波形分析相结合的方法处理信号,并通过点蚀形貌观察进行验证。结果表明声发射撞击和能量随时间逐渐增加,在某一时段达到峰值,随后下降并维持平稳状态。信号波形主要由幅度、能量较大的低频段(<100kHz)弯曲波和幅度、能量较小的高频段(>100 kHz)扩展波构成。研究结果对304不锈钢高于常温条件下声发射点蚀监测具有一定意义。      

Receiver operating characteristic analysis for the selection of threshold values for detection of capping in powder compression

The acoustic emission (AE) energy obtained from compressing lactose powder to form pharmaceutical tablets was chosen for condition monitoring of the tablets. The method used was based on the setting of an AE energy decision threshold such that problems of tablet capping and lamination were successfully identified. Capping and lamination are the most common types of problem that can occur in tablets manufacturing using a powder compression process. To assess the performance of a classifier, use was made of a receiver operating characteristic curve (ROC) obtained by plotting the correct detection probability against the false alarm probability based on AE energy distributions for capped and non-capped tablets. The area under the ROC curve, referred to as the AUC, determines the level of competency of the classifier. A value of 0.5 suggests a mere hazarding of guesses whilst a value of 1 indicates correct classification every time. The AE energy approach for tablet capping monitoring gives an AUC value of 0.96, thereby suggesting the possibility of a highly accurate classifier. With the assumption that penalties for false alarm and missed detection are equally severe, using the graphical method of expected penalty cost (EPC), the optimal AE energy decision threshold was established to be 1.2x10(8) units, at which the maximum correct capping detection rate of 95% was achieved. The paper also explains how a decision threshold can be obtained when the two penalties are not equal.     

光纤Sagnac传感器对材料频率特性的实验研究

不同的材料由于物理性质不同,在断裂时会产生不尽相同的声发射信号。光纤声发射传感器以其频带宽,抗电磁干扰,灵敏度高,体积小等优点在声发射（AE）信号探测方面有着广泛的应用前景。本文采用光纤Sangac传感器,对不同材料断裂过程的声发射信号进行检测,通过快速傅立叶变换分析其频谱。实验得知同一种材料断裂的声发射信号具有相同的...     

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.     

Shaft angular misalignment detection using acoustic emission

Shaft angular misalignment (SAM) is a common and crucial problem in rotating machinery. Misalignment can produce several shortcomings such as premature bearing failure, increase in energy consumption, excessive seal lubricant leakage and coupling failure. Vibration analysis has been traditionally used to detect SAM; however, it presents some drawbacks i.e. high influence of machine operational conditions and strong impact of the coupling type and stiffness on vibration spectra. This paper presents an extensive experimental investigation in order to evaluate the possibility of detecting SAM, using acoustic emission (AE) technique. The test rig was operated at under different operational conditions of load and speed in order to evaluate the impact on the AE and vibration signature under normal operating conditions. To the best of the author’s knowledge, this is the first attempt to use AE for the detection of SAM under varying operational conditions. A comparative study of vibration and AE was carried out to demonstrate the potentially better performance of AE. The experimental results show that AE technique can be used as a reliable technique for SAM detection, providing enhancements over vibration analysis.     

基于光纤耦合器的声发射传感器

为检测变压器内局部放电产生的声发射信号,介绍了一种基于特殊光纤熔融拉锥耦合器型声发射传感器。它是利用声波引起的扰动改变耦合器两臂光功率输出的特点来检测声发射信号。实验结果表明：此种传感器在10kHz～250kHz范围内对声发射信号有良好响应,在155kHz灵敏度为5.6×10^-6V/Pa,噪声为1.8Pa声压,有望在复合材料与结构、电力无损检测方面得到应用。     

不同浓度的尾砂胶结充填体破坏过程 声发射特性试验研究*

制备了3种不同质量浓度的充填体试件,进行了单轴压缩声发射试验,分析了不同浓度的充填体力学特性,重点研究了试件破坏过程中的声发射振铃计数、声发射累计撞击数与声发射累计能量的比值(r值)、主频及其相对高频信号激增响应系数特征。研究表明：随着浓度的增加,充填体的峰值强度与弹性模量呈增大趋势,充填体中出现的声发射累计振铃计数越多;r值先升高再持续减小到一个较低值,随着外载荷的增加,进入缓慢升高阶段,峰值前均保持在该阶段。充填体破裂前兆信息在声发射信号主频分布中呈现主频段增多现象,表现为由加载初期的1~2个主频段,在临界主破裂时增多到3~5个主频段;且随着浓度的增加,声发射信号主频频段分布越宽,声发射相对高频信号(160~180 kHz)的激增响应系数呈递减趋势。以上特征可为不同浓度的尾砂胶结充填体稳定性监测、预测提供依据。     

用埋入式光纤传感器探测建筑结构中的声发射

声发射技术已经应用于金属和混凝土结构中,作为探测内部裂缝的一种无损检测方法。目前用的技术都是由压电换能器来采集声发射信号。讨论了基于用光纤技术的声发射传感器的开发和测量方法。它是用埋入式光纤传感器来监测类似桥梁、高速公路、隧道和房屋建筑等混凝土结构中的开裂信号。