Second-order coupled tristable stochastic resonance and its application in bearing fault detection under different noises

Bearing fault is the most likely to occur in mechanical fault, and stochastic resonance (SR), as a noise enhanced signal processing tool, can find mechanical faults as early as possible, so as to avoid larger problems. However, most of the existing research methods are based on the first-order Langevin equation. According to the previous studies of many scholars, the weak signal detection ability of the second-order system is better than that of the first-order system, and the coupled system also has better performance due to the addition of the control system. So, in order to detect the fault signal more easily, a second-order coupled tristable stochastic resonance system (SCTSR) based on the adaptive genetic algorithm (AGA) is proposed, it is an improvement on improving the first-order coupled tristable stochastic resonance system (FCTSR). First, based on the fourth-order Runge–Kutta algorithm (F-RK), the performances of monostable, bistable and tristable control systems to SCTSR are compared, it is verified that the monostable system has the best performance as SCTSR’s control system. Secondly, the equivalent potential function of SCTSR is derived, and the influences of each system parameters on it are researched. The output signal-to-noise ratio gain (SNRG) is chosen as a measure to verify that SCTSR’s performance is better than that of FCTSR, and the influences of parameters on SNRG are discussed. SCTSR and FCTSR are used to detect low-, high- and multi-frequency cosine signals combined with AGA. The simulation results are compared with the wavelet transform method, which proves the performance superiority of SR, and also prove that SCTSR is easier to detect weak signals and has a stronger de-noising ability. Finally, SCTSR and FCTSR are applied in bearing fault detection under Gaussian white noise and trichotomous noise. The results also prove that SCTSR can get larger peaks and SNRG, and it is easier to detect fault signals. This proves that SCTSR’s performance is superior that of other methods in bearing fault detection, and has better engineering application value.

     

Unknown bearing fault diagnosis under time-varying speed conditions and strong noise background

The bearing vibration signal shows strong non-stationary property under time-varying speed conditions. In addition, the weak bearing fault characteristic is often submerged in strong background noise. How to accurately extract the unknown fault characteristic from the non-stationary vibration signal is the primary problem of bearing fault diagnosis. Stochastic resonance has been proved to be an effective weak signal enhancement method. Therefore, an unknown bearing fault detection technology of speed variation is proposed, which breaks through the periodicity limitation of the classical stochastic resonance on the input signal. It enables stochastic resonance suitable for the enhancement of non-stationary fault signal. Firstly, the non-stationary vibration signal is processed by the computed order tracking to obtain the stationary signal in angular domain. To extract the potential feature information, the bearing imaginary fault order index is constructed from the angular domain order spectrum. Then, the resonance response at the imaginary fault order is obtained. Finally, the coherence resonance theory is introduced to judge the bearing fault pattern through the resonance factor index of response order spectrum. The proposed method overcomes the fuzzy mapping relationship between the signal symptom and the bearing fault caused by speed variation. The experimental data analysis results provide effective support for the proposed method.

     

汽车微弱振动信号的混沌振子识别

混沌振子对微弱信号的检测在实际应用中具有重要价值。论文介绍了微弱信号检测的混沌振子理论，提出了混沌振子的幅频联调自适应方法，用以识别微弱信号，并给出了幅频联调自适应方法的具体步骤．对淹没在强噪声中微弱周期信号混沌检测进行了仿真分析。针对汽车在运行中飞轮壳出现裂纹的问题，用混沌振子进行了实际微弱振动信号识别。与相关的其它研究进行对比，确定所识别出的单周期微弱振动信号，说明了该裂纹的出现，该项研究可应用于汽车各个部件的隐蔽性故障分析。     

Detecting the weak high-frequency character signal by vibrational resonance in the Duffing oscillator

Nonlinear Dynamics - The weak high-frequency character signal (HCS) cannot be detected substantially by the traditional vibrational resonance (VR) theory. In this paper, by introducing the scale...     

When underwater degraded images meet logical stochastic resonance

Owing to light attenuation and high background noise, underwater images are significantly degraded, which hiders the development of underwater exploration. However, noise itself can be used to counter noise. In this paper, we apply logical stochastic resonance (LSR) to help detect weak objects from low-quality underwater images. On the basis of analysis of the physical character of underwater images, three models, namely basic dynamical system driven by Gaussian noise, basic dynamical system driven by Ornstein–Uhlenbeck (OU) noise, and dynamical system with extra delay loop, are chosen to study the performance of LSR-based object detection. The main workflow of LSR-based object detection is introduced. To analyze the performance of LSR, we perform explicit experiments and systematically discuss the interplay of additional noise with the system parameters. LSR is proven to be helpful in detecting weak objects from low-quality underwater images. Both OU noise and extra delay loop will help the whole system to maintain stability in a higher noisy background.     

滚动轴承早期故障的小波诊断方法

利用包络分析结合小波变换抽取强背景噪声下滚动轴承振动信号中的故障信息,对滚动轴承早期故障进行诊断,对五套３０７号轴承进行的成功诊断表明,提出的方法准确有效,适用于滚动轴承的在线监测与诊断。     

Simplified coded dispersion entropy: a nonlinear metric for signal analysis

Recently, coded permutation entropy has been proposed, which enhances the noise immunity by quadratic partitioning on the basis of permutation entropy. However, coded permutation entropy and permutation entropy only consider the order of amplitude values and ignore some information related to amplitude. To overcome these defects, this paper applies the concept of quadratic partitioning to dispersion entropy (DE), takes advantage of the fact that DE can effectively measure amplitude information, and proposes coded DE (CDE), which increases the number of patterns and improves the divisibility by further coding the dispersion patterns in DE. Moreover, to reduce the computational consumption of CDE, we simplify the division criterion in quadratic partitioning while guaranteeing that no effective information is lost and propose simplified CDE (SCDE). Several simulation experiments demonstrate the advantages of SCDE and CDE over DE, permutation entropy, and coded permutation entropy in detecting the nonlinear dynamic changes within chaotic and synthetic signals. In addition, real-world experiments on electroencephalogram signals, bearing signals, and ship signals show that SCDE has better performance in medical diagnosis, fault diagnosis and signal classification, and the accuracy of SCDE-based classification methods is higher than that of other entropy-based methods.

     

基于时频变换的激波风洞天平信号分析与处理

在激波风洞中开展测力试验时,测力系统在风洞流场起动瞬间会受到冲击激励,从而对天平的输出信号产生惯性干扰.天平输出信号中叠加有动态气动力信号和惯性振动信号,有可能无法直接分辨出气动力信号的规律性,信号处理结果与真实气动力之间会产生较大的误差,导致处理结果不可靠.由于模型测力天平系统结构的复杂性,在极短的有效试验时间(毫秒...     

深孔台阶爆破近区振动信号趋势项去除方法

基于深孔台阶爆破近区大量实测振动信号,总结了趋势项产生的原因主要为大振幅脉冲输入下的非线性失真及低频干扰叠加,在此基础上以测试仪器有效监测范围作为识别趋势项组成部分的判别准则。利用集合经验模态分解（ensemble empirical mode decomposition,EEMD）、小波分解等信号分析手段,提出了以固有模态函数（intrinsic mode function,IMF）的频带分布为指标、人工判别的趋势项去除方法,以及基于自相关分析识别噪声特征的小波阈值去噪方法。实例证明该方法切实有效,可实现爆破信号的批量化预处理。     

Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

In machine defect detection, namely those of gears, the major problem is isolating the defect signature from the measured signal, especially where there is significant background noise or multiple machine components. This article presents a method of gear defect detection based on the combination of Wavelet Multi-resolution Analysis and the Hilbert transform. The pairing of these techniques allows simultaneous filtering and denoising, along with the possibility of detecting transitory phenomena, as well as a demodulation. This paper presents a numerical simulation of the requisite mathematical model followed by its experimental application of acceleration signals measured on defective gears on a laboratory test rig. Signals were collected under various gear operating conditions, including defect size, rotational speed, and frequency bandwidth. The proposed method compares favourably to commonly used analysis tools, with the advantage of enabling defect frequency isolation, thereby allowing detection of even small or combined defects.     

微磁检测应力和塑性区的磁弹塑耦合理论

金属磁记忆微磁检测方法, 利用铁磁材料局部磁性状态的变化, 进行应力集中或塑性区域位置及程度的检测与评价. 面向微磁信号的定量理论分析可对其工程领域应用提供重要指导. 本文介绍铁磁材料微弱环境磁场下的磁弹塑性本构进展, 及其在微磁信号分析方面的应用. 力磁本构关系方面, 针对微磁检测弱磁化条件, 基于有效场理论构建了受弹塑性载荷铁磁材料的理想磁化本构的显式解析式, 并结合接近原理分析了恒定外加微弱磁场下应力-应变对材料磁化强度的影响. 检测信号分析方面, 基于弹性力学理论、静磁学理论和新建立的磁弹塑性本构关系, 建立并求解了微弱磁场下铁磁试件中弹性应力或塑性区诱导的表面磁信号的二维分析模型. 结合实验结果证实其在刻画弹塑性因素对微磁信号影响规律方面的能力, 并详细分析了微磁信号的特征量与局部弹性应力或塑性区的尺寸间的相关关系. 相比已有力磁本构关系, 本文建立的显式解析形式的理想磁化更加简洁, 有助于提升对力磁耦合效应的定量化理解和应用.      

Condition Monitoring of Robot Joints Using Statistical and Nonlinear Dynamics Tools

This paper considers the problem for condition monitoring of robot joints employing measured acceleration signals. The study aims at (1) Determining features, to be extracted directly from the measured acceleration signals, to detect defects in robot joints and at (2) Finding features dependent on the size of the fault in order to quantify the present defects. The signals coming from intact robot joints and from joints containing backlash or clearance are analyzed using nonlinear dynamics as well as statistical tools. A method for defect detection that employs nonlinear autoregressive (AR) modeling of the acceleration signals is successfully applied to detect backlash and clearance in robot joints. Two procedures for defect quantification are considered – one of them based on the AR modeling and the other employing nonlinear dynamics and statistical features. The problems are considered in the context of a pattern recognition paradigm.     

旋转加速度计式重力梯度仪输出解调与滤波

重力梯度仪是对地球表面微小重力梯度变化进行连续测量的仪器。由于核心敏感元件加速度计工艺与性能水平限制,以及多环节安装误差等因素导致系统实际输出信号中包含了大量噪声,且信噪比极低,为了能够在强噪声中有效提取真实的重力梯度信号,需在信号解调过程中降低谐波干扰引起的测量偏差。结合误差产生机理,分析比较了不同的解调方法对重力梯度信号解调的影响,明确了合理的信号解调手段。同时,在数据处理过程中增加带通滤波环节,进一步降低动态噪声对系统的影响。结合动态摇摆实验数据的仿真验证结果表明:上述措施对重力梯度仪原始观测数据的噪声抑制具有明显效果,测量偏差在一定程度上得到解决,解调后信号的噪声幅度下降至20%,提高了系统的空间分辨率,具有工程化应用意义。     

与CRP相结合的SAFT在混凝土损伤检测成像中的研究

将传统合成孔径聚焦技术(Synthetic aperture focusing technique,SAFT)与共反射点(Common reflection point,CRP)信号叠加法相结合应用于混凝土损伤检测中,可提高在强噪声环境中拾取缺陷回波信号的能力。本文采用二维有限元仿真验证了该思想的合理性,通过混凝土实验检验了该方法在实际应用中的可行性和有效性。与传统SAFT成像结果相比,应用CRP信号叠加法的SAFT提高了成像横向分辨率和损伤定位精度,为工程应用提供了理论和实验参考。     

强跟踪CKF及其在惯导系统初始对准中的应用

容积卡尔曼滤波(CKF)是常用的惯性导航系统(INS)初始对准算法。针对在模型失配和观测噪声干扰情况下常规容积卡尔曼滤波出现精度下降甚至发散的问题,提出了一种自适应渐消滤波算法,引入多重渐消因子对预测误差协方差阵进行调整。设计了基于滤波残差序列统计特性的滤波状态x~2检验条件,检测滤波器故障并确定是否引入渐消因子,使渐消因子的引入时机更加合理,有效增强了算法的自适应性。仿真试验表明,新算法可以有效提高初始对准精度及鲁棒性。     

结构损伤的小波分形神经网络检测

用神经网络进行结构损伤检测、分析的有效性在很大程度上取决于训练样本的好坏。小波变换在时域和频域都具有表征信号局部特征的能力,小波包分析利用可以伸缩和平移的可变视窗能够聚焦到信号的任意细节,因此对有损伤的结构的非线性动力特性能进行有效的分析。利用分形几何方法不依赖于系统的数学模型的特点,将分形维数与小波分析相结合,建立了结构损伤的小波分形神经网络检测方法。研究结果表明,结构不同状态下的振动信号的各频段分形维数有明显的不同,可以将振动信号的各频段分形维数作为结构损伤检测的特征量,并用神经网络将结构的不同状态模式识别出来。     

基于最优奇偶向量检测的周跳检测

正确地检测和修复周跳是载波相位测量数据处理中的重要问题，中将周跳作为GPS卫星信号故障的一种特殊形式，把周跳检测问题转化为GPS载波相位测量的故障检测问题。这里采用鲁棒性好的最优奇偶向量方法检测周跳并进行修复。通过仿真结果验汪了该方法即使在动态情况下也可以有效地检测并修复小周跳。     

螺栓松紧程度的受控敲击检测方法

螺栓连接是工程中大量使用的装配形式.在振动等环境因素的作用下,螺栓很可能会发生松动,这样不但会影响结构的正常功能,严重时还会导致安全事故.因此,螺栓松紧程度的无损检测方法一直受到研究者的关注.为了发展一种简单有效的检测方法,本文回避了复杂的非线性动力学分析,而直接根据螺栓敲击装置加速度功率密度谱中的敏感频段来检测螺栓的松动情况.另外,本方法要求对敲击力进行严格控制,以保证激励的可重复性,这样就可以结合支持向量机方法来定量地检测螺栓的松紧程度.实验表明,方法可以较好地实现对螺栓的松紧程度的定量检测,特别是对于螺栓全松和螺栓刚开始松动这两种情况,可以达到较高的检测精度.     

耦合故障转子系统中裂纹信息的诊断

旋转机械中,当裂纹与其他故障并存形成耦合故障时,裂纹信息往往被其他故障的信息所掩盖,从而难以从信号特征上诊断出裂纹故障。利用裂纹故障引起的等效外加弯矩特性,采用基于模型的故障诊断方法,可以诊断出耦合故障中的裂纹故障信息。以两种最常见的耦合故障—裂纹碰摩耦合故障、裂纹松动耦合故障为例,采用基于模型的诊断方法诊断耦合故障中的裂纹信息,取得了比较好的效果,并进行试验来验证理论结果。     

结构健康监测中的损伤检测技术研究进展

对结构健康监测研究中的结构损伤检测方法及其特点进行了介绍.从基于结构模态分析的方法和基于结构动态试验信号处理的方法两方面,阐述了结构健康监测中的损伤检测方法及其最新研究进展.基于结构模态分析的结构损伤检测方法是针对整个结构的检测,使用的模态都限于低阶模态范围内,所检测的结构应容易建立有限元模型,便于进行响应预测.基于结构动态试验信号处理的损伤检测方法通常是针对结构局部构件的损伤检测,不需要对结构进行有限元建模,而直接从测试的动态响应信号中提取表征结构损伤的特征参数.文中提出了对比性损伤检测方法和非对比性损伤检测方法的概念,并对结构损伤检测中常用的信息传感与处理技术进行了论述,指出了结构损伤检测研究中应该考虑的传感器布置问题.提出了将损伤信息的主动检测与被动检测相结合进行损伤程度判断和剩余寿命估计等问题是有待进一步深入研究的课题.