基于振动和声发射技术的滚动轴承故障分析

对声发射和振动两种方法在滚动轴承裂纹故障诊断中的应用进行了研究,并比较了这两种方法在早期故障诊断中的优越性。采用电火花技术在滚动轴承内圈加工宽度为0.5 mm和0.8 mm的两种裂纹,模拟在实际情况下的滚动轴承内圈裂纹故障。在低速(10 r/min)和高速(1 000 r/min)两种情况下,通过试验分别测得滚动轴承两种裂纹故障状态下的声发射信号和振动信号。分析比较了不同转速、不同裂纹故障状态下的轴承振动和声发射信号的时频特征。对信号数据进行波形和包络谱分析。试验结果表明:声发射信号与调制信号无关。对声发射信号进行包络分析,可以有效分析信号的故障特征频率。在故障微弱情况下,声发射信号比振动信号更具敏感性;当故障强度较大时,采用振动方法诊断更为准确,即声发射技术更有利于故障的早期检测。声发射技术可作为振动分析技术的补充,用于故障检测。     

不同充放电倍率下的锂电池声发射信号分析

锂离子电池充放电倍率是影响电池老化的主要外部应力之一,充放电倍率过大可能使电池内部电流分布不均,导致锂沉积或电极活性材料结构疲劳变形。基于声发射方法研究了不同充放电倍率下的电池状态。通过设计锂离子电池声发射信号试验平台,采集了不同充放电倍率下的电池声发射信号。对声发射信号进行参数与波形分析,发现声发射信号主要存在两个突发型的波形,且充电与放电的声发射信号波形的初次穿越阈值的采样点符号相反。锂离子电池声发射信号的幅度和波形时间间隔都与电池的充放电倍率相关,电池充放电倍率越大,声发射信号幅度越高,波形时间间隔越小。     

基于小波变换的岩体AE波形特征研究

文章对室内声发射事件能率与应力的关系及波形特性进行了分析。通过小波变换,将室内岩体声发射等波形分解成低频和高频信号,对其原始声发射波形、细节信号和近似信号进行比较与分析,结果表明岩体在变形损伤破裂过程中不同时期的声发射波形特征不同。据此,可建立声发射波形特征库,用于预测预报岩体受损变形过程的发展趋势和规律。对含有信号部分的小波系数进行重构后,该信号与原始信号的误差小于10-10,说明小波分析可使岩体声发射波形特征预测预报的准确度和可靠性得以提高。     

列车转向架侧架声发射检测的有限元仿真与试验

侧架作为列车转向架的关键部件,其结构疲劳性能和裂纹状态直接决定着机车车 辆的运行安全。首先从侧架检修时易出现疲劳裂纹的典型位置入手,提出采用声发射技术的必 要性;其次集合UG、HyperMesh、ANSYS/LS-DYNA 3 种有限元软件的三维建模方法,对声发 射信号在侧架中的传播特性进行仿真和可视化;最后依据仿真分析结果提出试验方案,并搭建 侧架结构的声发射断铅试验系统。检验了声发射技术在铁路大型复杂构件上应用的可行性。     

声发射技术在立柱缸体裂纹监测中的应用研究

针对立柱缸体在做强度试验和型式试验时出现的缸体破裂现象,提出了一种基于声发射技术的立柱缸体裂纹监测方案。该方案采用声发射技术捕捉、分析、处理和显示立柱在受压过程中产生的裂纹弹性波;采用时差定位法获得声发射源位置;采用小波包技术处理声发射信号,以进一步提高了定位精度。测试结果表明,声发射技术用于检测立柱缸体裂纹、焊缝等隐形机理的产生、发展和进行危险性分析是可行的。     

全抛釉砖单轴加载破坏声发射试验研究

为寻找更有效的全抛釉砖质量检测依据,对全抛釉砖提出更好的设计和使用方案,因此开展全抛釉砖单轴加载破坏全过程声发射试验。通过SAEU2S型数字声发射采集系统直接采集破坏全过程声发射信号波形,通过对不同状态下不同质量的全抛釉砖的振铃计数和能量分析处理,发现声发射信号产生的快慢、强弱及其变化过程与全抛釉砖的试验状态和质量有密切关系,并从声发射信号裂纹扩展角度阐释全抛釉砖破坏基理。     

改进声发射信号的桥梁焊缝裂纹识别仿真研究

针对因环境中存在过多噪声,导致桥梁焊缝裂纹识别精准度低的问题,提出基于声发射信号的桥梁焊缝裂纹识别方法。利用传感器提取桥梁周围的实时信号,通过信号在周期序列上的幅值变化,判定噪声信号,采用小波变换算法对噪声信号实施重构变换,建立硬阈值和软阈值函数,约束噪声信号。采用神经元传递函数计算原始信号序列中隐含层神经元的具体特征表现参数,得到信号的特征类间平均值,通过类间参数求得特征量。以带有声发射信号提取技术的传感器作为识别载体,将特征参数输入到识别传感器中,针对不同的桥梁测试点,建立焊缝裂纹识别通道,完成有效识别。实验结果证明,所提方法的识别精准度较高,无论是以持续频率还是持续时间信号作为测试指标,均能实现高效识别。     

单晶硅研磨过程的声发射在线监测研究

本文在BIN62型超精密研抛机的基础上设计了研磨过程的声发射在线监测装置,试验研究了不同研磨工况对声发射信号RMS值和材料去除率的影响规律,采用回归分析方法建立了材料去除率与声发射信号RMS值的线性数学模型,并通过声发射波形的频谱分析和表面形貌的观测研究了单晶硅研磨过程中的声发射源机制。结果表明：在保持其他研磨工况不变的条件下,声发射信号RMS值随着研磨压力或研磨速度的增加而增加;根据RMS值可实现材料去除率的在线监测,在给定研磨工况范围内材料去除率预测模型的预测误差小于4.2%;声发射波形的频谱分析技术可用于声发射源机制的识别,单晶硅研磨过程中声发射信号主要的频率成分出现在50 kHz~260 kHz频段内,声发射信号主要来源于材料的脆性解理、磨粒磨损和轻微粘结磨损。     

煤岩声发射信号识别研究

针对煤岩破裂的声发射信号难以在复杂的噪声环境中识别的问题,提出了一种基于小波包分析和小波特征能谱系数分析的煤岩声发射信号识别方法。选取Symlets小波作为煤岩声发射信号分析的小波基函数,采用混合阈值算法对该信号进行去噪处理,提取出有用声发射信号,并采用Matlab软件分别对有用声发射信号和噪声信号的小波包分解进行仿真,得到两者的小波特征能谱系数和小波包特征向量。仿真结果表明,有用声发射信号特征向量的各级能量变化程度较大,噪声信号特征向量的能量变化较为稳定,从而可实现煤岩声发射信号的识别。     

基于小波包分析木材声发射信号消噪处理

由于目前的滤噪技术在检测木材声发射信号时还存在一定的缺点,本文根据木材声发射信号的特征,基于小波包分析研究了利用信号的小波包分析、计算和最佳小波包基的选取.采用默认阈值方法处理小波系数,通过小波包重构得到消噪后的木材损伤声发射信号,噪声得到较好的抑制.结果表明用小波包变换进行消噪处理,噪声消除彻底,提高了损伤缺陷检测的准确性.     

AE behaviors evaluation with bp neural network

This paper reports our experiment on training a three layer forwards neural network with backpropagation algorithm (BP) to memorise acoustic emission (AE) behaviors of magnesium alloy during fatigue test process and perform the mapping from AE behaviors to fatigue crack propagation. This study reveals the potential possibility of using artificial neural networks for automated AE testing. Simulation results are very encouraging and conclusively in favor of this attempt.     

Passive acoustic monitoring of head disc interface interactions

A hard disc drive (HDD) recording head thermal protrusion is monitored by the passive acoustic characterization technique where adaptive discrete wavelet (ADW) filtering has been introduced to declare the contact. A phenomenological model is built to demonstrate detectability of the passive acoustic monitoring. The model is based on the mechanical impedance approach where impedances of head disc interface (HDI) and acoustic emission (AE) sensor are compared for matching over air bearing/head gimble assembly frequency bandwidth. A synthesized HDI response signal derived from the HDI mechanical impedance function is compared to the real AE signal obtained during the thermal protrusion based contact detection. A methodology of HDD level AE signal characterization presented in this work consists of the ADW filtering technique where the fifth order DB7 wavelet base function is used in AE signal decomposition. The signal decomposition order is selected by the AE signal entropy minimization.     

吉林省西部区域蒸散时空变化分析

基于地表能量平衡理论,利用NOAA/AVHRR数据,采用SEBS模型,计算了研究区15年地表蒸散量,从年、季度和月等三个时间尺度对其进行时空变化分析。结果显示:(1)各年平均蒸散量相差较大,最大的是1988年,最小的是1996年;月平均蒸散量最大值出现在5月,最小值出现在12月,形成一单峰型曲线;第二季度平均蒸散量最大,第四季度最小,其分布曲线也为单峰型。(2)多年平均蒸散量的空间分布东半部明显大于西半部,最大的是扶余县,最小的是通榆县;各市县的月平均蒸散量分布仍为单峰型曲线,在5月达到最大值,12月最小,与全区的月平均蒸散量分布曲线一致;各市县第一季度和第四季度平均蒸散量相差不大,第二和第三季度相差较大,但总体分布趋势与全区一致,仍为单峰型曲线。以上结果表明:研究区区域蒸散时空分布极不均匀,强烈的蒸散作用为研究区生态环境恶化提供了有利条件。     

Crack propagation assessment for spur gears using model-based analysis and simulation

Model-based gear dynamic analysis and simulation has been a promising way for developing effective gearbox vibration monitoring approaches. In this paper, based on the dynamic model of a one-stage gearbox with spur gears and one tooth crack, statistical indicators and the discrete wavelet transform (DWT) technique are investigated to identify effective and sensitive health indicators for reflecting the crack propagation level. The results suggest that the root mean square (RMS) indicator is a better statistical indicator than the Kurtosis indicator to reflect the crack propagation in the early stage; the RMS indicator based on the residual signal segments that are strongly affected by the crack is more sensitive; the proposed DWT approach can improve the sensitivity of the RMS indicator, and the RMS indicator becomes more sensitive with the increase of the DWT level up to a best DWT level, beyond which either the monotonicity is lost or the sensitivity decreases; the proposed approach is effective with the presence of noise; with the increase of the noise level, the DWT level at which the best performance is achieved, and thus the sensitivity, decreases. Gearbox systems with different sizes and different input shaft frequencies are also investigated, and it is found that the observations presented above hold for different gearbox system settings.     

Fast and robust level set update for 3D non-planar X-FEM crack propagation modelling

In the X-FEM framework, the need to represent a discontinuity independently of the structural mesh relies on the level set technique. Hence crack propagation can be simulated by an update of two distinct level sets, the evolution of which is described by differential equations. The aim of this paper is to analyse the resolution of these equations in order to formulate a robust and fast numerical process allowing 3D crack propagation simulations even in presence of high kink angles occurring in mixed mode propagation. The numerical integration is accomplished by means of a robust finite difference upwind scheme applied to an auxiliary regular grid. An alternative level set update equation and a fast localisation of the integration domain, specifically developed for crack propagation problems, are formulated and proposed in the paper in order to gain in stability, robustness and performance.     

经验模态分解和AR模型在飞行器健康诊断中的应用

为了有效地诊断飞行器的健康状况,提出了一种基于EMD-AR模型和PNN的飞行器健康诊断新方法。该方法采用EMD（Empirical Mode Decomposition,EMD）将飞行器关键部件的声发射信号进行分解,得到多个内禀模态分量（Intrinsic Mode Function,IMF）,对前两个IMF分量建立AR模型,采用U-C算法对AR模型进行参数估计,以模型主要的自回归参数和残差的方差构建特征向量;运用概率神经网络（Probabilistic Neural Network,PNN）对飞行器的健康状态进行诊断。通过对某型号真实飞行器关键结构部件的健康监测实验表明,该方法可以有效地诊断出飞行器关键结构部件的疲劳裂纹,从而证明了该方法的有效性。     

Low bit-rate speech coding based on multicomponent AFM signal model

In this paper, we propose a novel multicomponent amplitude and frequency modulated (AFM) signal model for parametric representation of speech phonemes. An efficient technique is developed for parameter estimation of the proposed model. The Fourier–Bessel series expansion is used to separate a multicomponent speech signal into a set of individual components. The discrete energy separation algorithm is used to extract the amplitude envelope (AE) and the instantaneous frequency (IF) of each component of the speech signal. Then, the parameter estimation of the proposed AFM signal model is carried out by analysing the AE and IF parts of the signal component. The developed model is found to be suitable for representation of an entire speech phoneme (voiced or unvoiced) irrespective of its time duration, and the model is shown to be applicable for low bit-rate speech coding. The symmetric Itakura–Saito and the root-mean-square log-spectral distance measures are used for comparison of the original and reconstructed speech signals.     

Numerical modeling of crack reorientation and link-up

The FRANC3D/BES software system has been used to simulate the reorientation and link-up of hydraulic fractures in three-dimensional (3D) problems. The adopted technique only needs to discretize the body surface and the crack surface. The crack propagation direction is determined using the minimum strain energy criterion. Crack propagation amount is calculated using the mode I stress intensity factor. In hydraulic fracturing, the number of multiple cracks for a given number of perforations depends on the resulting interaction of the cracks. The interaction may be expressed by the fracture stiffness which has been obtained for 3D problems in this paper.     

Analytical and experimental validations of a numerical band-limited Green’s function approach for modelling acoustic emission waves

Dynamic Green’s functions are essential for modelling acoustic emission (AE) wave propagation and for the quantitative characterisation of AE sources. In this work, a method for evaluating the Green’s function of a body using the finite element method is presented. The advantage of the proposed method is that it can be used to model realistic geometries, material properties and sources that cannot be treated analytically. The numerical results presented in this paper are compared with known analytical solutions of the Green’s function for an infinite isotropic plate and also with experimental measurements of AE waves generated by known artificial AE sources (ball impact and pencil lead break).     

Artificial neural network for prediction of air flow in a single rock joint

In this paper, an attempt has been made to evaluate and predict the air flow rate in triaxial conditions at various confining pressures incorporating cell pressure, air inlet pressure, and air outlet pressure using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network having 3-7-1 architecture network was trained using 37 data sets measured from laboratory investigation. Ten new data sets were used for the, validation and comparison of the air flow rate by ANN and multi-variate regression analysis (MVRA) to develop more confidence on the proposed method. Results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between measured and predicted values of air flow rate. It was found that CoD between measured and predicted air flow rate was 0.995 and 0.758 by ANN and MVRA, respectively, whereas MAE was 0.0413 and 0.1876.