A time domain approach to diagnose gearbox fault based on measured vibration signals

Spectral analysis techniques to process vibration measurements have been widely studied to characterize the state of gearboxes. However, in practice, the modulated sidebands resulting from the local gear fault are often difficult to extract accurately from an ambiguous/blurred measured vibration spectrum due to the limited frequency resolution and small fluctuations in the operating speed of the machine that often occurs in an industrial environment. To address this issue, a new time-domain diagnostic algorithm is developed and presented herein for monitoring of gear faults, which shows an improved fault extraction capability from such measured vibration signals. This new time-domain fault detection method combines the fast dynamic time warping (Fast DTW) as well as the correlated kurtosis (CK) techniques to characterize the local gear fault, and identify the corresponding faulty gear and its position. Fast DTW is employed to extract the periodic impulse excitations caused from the faulty gear tooth using an estimated reference signal that has the same frequency as the nominal gear mesh harmonic and is built using vibration characteristics of the gearbox operation under presumed healthy conditions. This technique is beneficial in practical analysis to highlight sideband patterns in situations where data is often contaminated by process/measurement noises and small fluctuations in operating speeds that occur even at otherwise presumed steady-state conditions. The extracted signal is then resampled for subsequent diagnostic analysis using CK technique. CK takes advantages of the periodicity of the geared faults; it is used to identify the position of the local gear fault in the gearbox. Based on simulated gear vibration signals, the Fast DTW and CK based approach is shown to be useful for condition monitoring in both fixed axis as well as epicyclic gearboxes. Finally the effectiveness of the proposed method in fault detection of gears is validated using experimental signals from a planetary gearbox test rig. For fault detection in planetary gear-sets, a window function is introduced to account for the planet motion with respect to the fixed sensor, which is experimentally determined and is later employed for the estimation of reference signal used in Fast DTW algorithm.     

应用半正定规划的目标方位超分辨方法

针对水下目标方位超分辨估计问题,提出了一种基于半正定规划(Sdp)的常规波束(CBF)方位超分辨算法(SdpCBF).Sdp-CBF算法基于常规波束形成获得多目标方位谱数据,利用阵列响应矩阵和半正定规划技术,精确估计目标数量和波达角方向.该算法的本质是利用阵列特性和信号能量信息获得超分辨方位估计,不用进行子空间分解,通过卷积反演的方式将阵列孔径的有限效应消除,在L₂范数约束条件下重构空间谱.仿真表明,Sdp-CBF算法具有较强的噪声抑制能力,对非相干和相干信号均具有目标方位超分辨能力,在低信噪比环境下的方位分辨性能超过多重信号分类(MUSIC)等经典高分辨算法。对消声水池以及湖上实验数据的处理结果显示,Sdp-CBF算法在复杂环境中对相干信号及微弱信号具有较强的分辨能力。      

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.     

爆轰流体力学模型敏感度分析与模型确认

验证、确认与不确定度量化(VVUQ)是评估物理模型可信度和量化复杂工程数值模拟结果置信度的系统方法.验证是要回答数值模拟程序是否正确求解了物理模型和程序是否正确实施或给出求解模型的误差、不确定性大小及使用范围,确认是要通过数值结果回答物理模型是否反映了真实客观世界或反映真实客观世界的可信程度.文章围绕爆轰流体力学模型,剖析了模型中不确定性因素,给出了影响模拟结果不确定性的关键因素清单,并对其开展了敏感度分析,确认了模型的适应性.     

Complex signal analysis for wind turbine planetary gearbox fault diagnosis via iterative atomic decomposition thresholding

The vibration signals from complex structures such as wind turbine (WT) planetary gearboxes are intricate. Reliable analysis of such signals is the key to success in fault detection and diagnosis for complex structures. The recently proposed iterative atomic decomposition thresholding (IADT) method has shown to be effective in extracting true constituent components of complicated signals and in suppressing background noise interferences. In this study, such properties of the IADT are exploited to analyze and extract the target signal components from complex signals with a focus on WT planetary gearboxes under constant running conditions. Fault diagnosis for WT planetary gearboxes has been a very important yet challenging issue due to their harsh working conditions and complex structures. Planetary gearbox fault diagnosis relies on detecting the presence of gear characteristic frequencies or monitoring their magnitude changes. However, a planetary gearbox vibration signal is a mixture of multiple complex components due to the unique structure, complex kinetics and background noise. As such, the IADT is applied to enhance the gear characteristic frequencies of interest, and thereby diagnose gear faults. Considering the spectral properties of planetary gearbox vibration signals, we propose to use Fourier dictionary in the IADT so as to match the harmonic waves in frequency domain and pinpoint the gear fault characteristic frequency. To reduce computing time and better target at more relevant signal components, we also suggest a criterion to estimate the number of sparse components to be used by the IADT. The performance of the proposed approach in planetary gearbox fault diagnosis has been evaluated through analyzing the numerically simulated, lab experimental and on-site collected signals. The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.     

Maximum likelihood estimation for second level fMRI data analysis with expectation trust region algorithm

The trust region method which originated from the Levenberg–Marquardt (LM) algorithm for mixed effect model estimation are considered in the context of second level functional magnetic resonance imaging (fMRI) data analysis. We first present the mathematical and optimization details of the method for the mixed effect model analysis, then we compare the proposed methods with the conventional expectation-maximization (EM) algorithm based on a series of datasets (synthetic and real human fMRI datasets). From simulation studies, we found a higher damping factor for the LM algorithm is better than lower damping factor for the fMRI data analysis. More importantly, in most cases, the expectation trust region algorithm is superior to the EM algorithm in terms of accuracy if the random effect variance is large. We also compare these algorithms on real human datasets which comprise repeated measures of fMRI in phased-encoded and random block experiment designs. We observed that the proposed method is faster in computation and robust to Gaussian noise for the fMRI analysis. The advantages and limitations of the suggested methods are discussed.     

A Simplified Formulation to Estimate Influence of Gearbox Parameters on the Rattle Noise

Occurrence of gear rattle in transmission systems can result in severe vibration and noise, which in applications such as automobiles is an important source of user discomfort. As a result , the reduction of the rattling noise has attracted lot of concerns. The rattling noise level is affected by several gearbox parameters, an understanding of which is essential to prevent the expensive design modifications at later stages of product development. To develop such understanding at the gearbox design stage, this paper analytically evaluates the gear parameters’ effect on the root mean square of the wheel gear acceleration under idling condition, which is known to be linearly correlated to the rattling noise level. Therefore, this evaluation allows for an investigation of the gear parameters’ influence on the rattling noise as well. This method is then verified by comparing the analytical results with the simulation results from a dynamic model built in SIMPACK as well as previously published experimental results. Thus, the proposed analytical evaluation method can optimize the gearbox specifications at the design stage to reduce the gear rattle noise level.     

Experimental three dimensional strain estimation from ultrasonic sectorial data

Most of the studies devoted to elastography are focused on the estimation of the axial component of the strain. However when subjected to any load, whatever the direction, soft biological media deform in the three spatial dimensions. The aim of our work is to build a three dimensional strain mapping from data acquired with a 3D clinical sectorial probe. The estimation of radial strain is based on the estimation of local scaling factors. A method of cross-correlation of interpolated signals between adjacent radiofrequency lines was used to estimate the angular displacement and strain. For the sectorial strain estimation, the same displacement estimation technique has been implemented. The method has been tested on experimental data acquired on calibrated phantoms and compared to simulation.     

A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox

The main gearbox is very important for the operation safety of helicopters, and the oil temperature reflects the health degree of the gearbox; therefore establishing an accurate oil temperature forecasting model is an important step for reliable fault detection. Firstly, in order to achieve accurate gearbox oil temperature forecasting, an improved deep deterministic policy gradient algorithm with a CNN–LSTM basic learner is proposed, which can excavate the complex relationship between oil temperature and working condition. Secondly, a reward incentive function is designed to accelerate the training time costs and to stabilize the model. Further, a variable variance exploration strategy is proposed to enable the agents of the model to fully explore the state space in the early training stage and to gradually converge in the training later stage. Thirdly, a multi-critics network structure is adopted to solve the problem of inaccurate Q-value estimation, which is the key to improving the prediction accuracy of the model. Finally, KDE is introduced to determine the fault threshold to judge whether the residual error is abnormal after EWMA processing. The experimental results show that the proposed model achieves higher prediction accuracy and shorter fault detection time costs.     

互谱域MVDR自适应时延估计方法研究

针对窄带信号,通过构造互谱时间序列,在互谱域建立了平稳时间序列时延估计的最小方差无畸变响应(MVDR)滤波器模型;利用分段近似处理,类比空间MVDR自适应算法,给出了其具体算法(Algorithm of MVDR in cross spectral domain,CSMVDR);进行了数值仿真实验研究和海上实验数据处理。数值仿真与实验数据处理结果初步验证了CSMVDR时延估计对于舰船辐射噪声的适用性,CSMVDR时延估计有比相关检测更好的时延估计性能,能够提高信噪比增益和时延估计精度。     

矢量声纳高速运动目标稳健高分辨方位估计

针对水声矢量信号处理框架中的高速运动目标低信噪 比小快拍条件下的稳健高分辨方位估计问题, 将压缩感知技术应用于水声矢量信号空间谱估计模型中. 结合声矢量传感器结构特性, 探讨了基于声压振速联合处理的广义时域滤波方法; 结合矩阵空域预滤波理论, 设计了基于阻带约束通带均方误差最大值最小的空域滤波器, 研究了矢量声纳空域预滤波方法; 结合以上分析, 提出了基于压缩感知技术的时空联合滤波高分辨方位估计方法, 给出了方法的数学模型、物理解释及具体实施步骤.理论分析和计算机仿真试验表明, 新方法对于小快拍数 条件下的矢量声纳高速运动目标高分辨方位估计问题, 具有较低的双目标分辨门限和较高的估计精度, 有着良好的应用前景.湖上试验验证了方法的有效性. 关键词： 声矢量传感器 空间谱估计 时空滤波 压缩感知     

顾及土壤湿度的土壤有机质高光谱预测模型传递研究

高光谱遥感技术作为当前遥感发展的前沿科技,通过电磁波与地物的相互作用,可以定量反演地物的物理化学性质。土壤有机质是重要的土壤养分信息参数,利用高光谱遥感技术快速获取其含量信息可以为精准农业的发展提供必要的数据支撑。然而,由于受到外部参数差异的干扰,导致建模精度降低的同时,还会造成已有模型传递性的“失效”。为了消除湿度差异的干扰,进一步拓展已有模型的适用空间,以江汉平原滨湖地区为例,通过对95个土壤样本进行加湿处理,在实验室自然风干的条件下,量测得到13套不同湿度等级土壤样本的可见—近红外反射光谱数据,建立了各湿度等级下土壤有机质的光谱反演模型,研究水分差异对建模精度的影响;在此基础上,运用Direct Standardization(DS)算法对湿土光谱进行校正,进而探讨该算法在提高模型传递性能方面的潜力。结果表明：基于风干土光谱建立的模型预测精度最高,未经校正的湿土光谱无法通过该模型进行土壤有机质含量预测,预测偏差在-8.34～3.32 g·kg-1,RPD在0.64～2.04;经过DS算法校正后的湿土光谱可以通过该模型进行土壤有机质含量预测,预测偏差降低至0,RPD值提高至7.01。研究表明DS算法能有效降低湿度差异对光谱反演土壤有机质的影响,使土壤有机质光谱反演模型适用于不同水分含量的土壤样本。     

Calibration of RPC-based muon detector at BESⅢ

The calibration algorithm for RPC-based muon detector at BESⅢ has been developed. The calibration method, calibration error and algorithm performance are studied. The primary results of efficiency and noise at layer, module and strip levels have been calibrated with cosmic ray data. The calibration constants are available for simulation and reconstruction tuning. The results of Monte Carlo and data are also compared to check the validation and reliability of the algorithm.     

基于多物理场计算的二极管雪崩击穿效应分析

基于电磁场、半导体物理及热力学方程的半导体器件多物理场模型方程组共同描述了器件内部的电热特性。而半导体器件在反向电压作用下,会发生碰撞电离及雪崩击穿现象。采用多物理场计算方法仿真型号为HSMS-282C的肖特基二极管的雪崩击穿现象,仿真结果与实验测量结果吻合,表明该算法能准确表征二极管物理特性,并对效应现象给出物理机理解释。     

颗粒离散元模拟中的阻尼系数标定

离散元模型中所采用的阻尼系数是不确定的,经常凭主观经验设定.为了使得物理模型与现实中的颗粒动力学属性符合更好,通过采用声频采样技术对物理模型中的阻尼系数进行标定,得出与实验符合的阻尼系数应为0.5左右.利用标定后的阻尼系数模拟了一元颗粒在圆柱形容器里的随机堆积过程,得到最终的堆积密度为0.625,与经典的实验结果一致.研究证明,在得到模型阻尼系数的同时,也确认了数值模型的正确性和用声频采样技术进行高精度碰撞时间检测的可行性. 关键词： 颗粒堆积 阻尼系数标定 声频采样 离散元法     

Surface elasticity and size effect on the vibrational behavior of silicon nanoresonators

Predominance of nano-scale effects observed in material behavior at small scales requires implementation of new simulation methods which are not merely based on classical continuum mechanic. On the other hand, although the atomistic modeling methods are capable of modeling nano-scale effects, due to the computational cost, they are not suitable for dynamic analysis of nano-structures. In this research, we aim to develop a continuum-based model for nano-beam vibrations which is capable of predicting the results of molecular dynamics (MD) simulations with considerably lower computational effort. In this classical-based modeling, the surface and core regions are taken to have different mechanical properties, where core atoms are assumed to have macroscale properties whereas surface layer is showing a different elastic modulus from the core components. By estimating physical parameters of proposed classical model using molecular dynamics results and the genetic algorithm, calibrated classical Euler–Bernoulli and Timoshenko beam models are developed. The results demonstrates that a Timoshenko beam model incorporating surface effects and having calibrated parameters, is able to provide almost the same results as molecular dynamics method which can be used to predict the vibrational behavior of nano-beams at different scales from nano to macro.     

基于点线对应的多摄像机全局位姿估计迭代算法

为了解决工业视觉场景中点和直线段共存情况下的多摄像机位姿估计问题,提出了基于点线特征对应的多摄像机全局位姿估计算法.以正交迭代算法为基础,使用多个固定在平台上的已标定摄像机间的几何约束,以全局方式处理多摄像机的重叠和非重叠视图的点和直线段特征,获得更加稳定精确的位姿估计.该算法先把所有摄像机数据进行统一表达,再把摄像机观测到的全部点特征的目标空间共线性误差和直线段特征的目标空间共面性误差之和作为误差函数,最后经数学推导得到使该误差函数最小化的迭代求解过程.实验结果验证了该算法的有效性以及优越性.     

基于BP神经网络的变速箱故障诊断方法研究

针对变速箱的工作时间不能真实反映实际健康状况的问题,通过提取变速箱的振动信号作为状态参数,建立了基于BP神经网络的变速箱故障诊断模型。该模型首先提取振动信号中对故障反映灵敏的成分作为特征值,获得BP神经网络的训练数据,并通过对比确定最优的隐含层节点数,确定BP神经网络的结构参数。模型训练结束后,以验证数据为例进行故障诊断研究,并对诊断结果进行评估。评估结果表明,该模型准确度高,具有较好的应用和推广价值。     

基于倒谱分析的单水听器目标运动参数估计

基于浅海射线声学多途结构,提出了一种具有高稳健性、高精度的单水听器目标运动参估计方法.针对匀速直线运动目标,综合多途时延差和运动学几何关系,构建了目标三维多途时延模型,进而获得目标运动参数与多途时延差的非线性时间映射.研究了典型水声信道的倒谱表达式,提出了利用倒谱提取多途时延差的策略.采用差分进化综合优化手段估计目标运动参数,提高了算法的稳健性.理论及仿真结果表明,倒谱的时间分辨率不受信号带宽的限制,而主要取决于信号类型和信噪比;CW信号的倒谱对多普勒不敏感;参数估计精度主要取决于时延差估计精度和参与差分进化运算的信息量,当包含有最近点信息时参数估计性能较好.水池实验结果进一步验证了方法的正确性和有效性.