多分类边界支持矩阵机及其在滚动轴承故障诊断中的应用

由于支持矩阵机(SMM)利用平行超平面实现对不同类别样本的分类,使其无法最大化任意两类样本之间间隔,为此,通过分析非平行超平面与支持矩阵机的相关理论,提出了一种多分类边界支持矩阵机(MBSMM),并将其应用于滚动轴承的故障诊断中.首先,在MBSMM中以矩阵为建模元素,建立了其多分类目标函数,充分利用输入矩阵行与列之间的...     

Weak fault feature extraction of rolling bearing under strong poisson noise and variable speed conditions

Fault feature extraction of the rolling bearing under strong background noise is always a difficult problem in bearing fault diagnosis. At present, most of the research focuses on weak signal extraction under Gaussian white noise and has certain practical significance. However, the noise in engineering is often complex and changeable, Gaussian white noise cannot fully simulate the actual strong background noise. Poisson white noise is a type of typical non-Gaussian noise, which widely exists in complex mechanical impact. It is of great significance to study the weak fault feature extraction of a faulty bearing under this type of noise. At the same time, variable speed conditions occupy most rotating machinery speed conditions. Non-stationary vibration signals make it difficult to extract fault features, and the frequency spectrum ambiguity will occur because of speed fluctuation. To solve the above problems, a method of weak feature extraction of a faulty bearing based on computed order analysis (COA) and adaptive stochastic resonance (SR) is proposed. Firstly, by numerical simulation, the non-stationary fault characteristic signal corrupted with strong Poisson noise is transformed into a stationary signal in the angle domain by COA. Secondly, the influence of the parameters of the pulse arrival rate and noise intensity of Poisson white noise on the optimal SR response in the angle domain are studied, and the influence of the parameters of Poisson white noise on the fault feature extraction is given. Then, adaptive SR method is used to extract and enhance fault feature information. Finally, the effectiveness of this method in weak fault characteristic signal extraction under strong Poisson noise is verified by experiments. Numerical simulation and experimental results verify the effectiveness of the proposed method in bearing fault diagnosis under strong Poisson noise and variable speed conditions.

     

变分非线性单分量 Chirp 模态提取及其在电机轴承诊断中的应用

变频驱动下旋转机械设备的振动信号具有调制成分复杂、涉及频带较宽和噪声干扰严重等问题,造成与故障相关的单分量调制成分提取困难。为此,提出了一种新的变分非线性单分量chirp模态提取(VNSCME)方法,建立单个目标模态解调频带最窄与残余分量能量最小组合约束的变分优化模型,迭代提取特定的单分量非线性调制成分。预设一个有关目标模态瞬时频率的先验知识,VNSCME能够独立地提取出特定的单分量调制成分并准确估计其瞬时频率。与现有研究相比,VNSCME具有不受时频分布分辨率限制、初始化简单和计算效率高的特点。将VNSCME与阶次跟踪技术相结合,应用于变频驱动电机轴承故障诊断。分别对仿真和实测的故障振动信号进行处理,结果显示瞬时转频估计的相对误差低于0.76%,提取单个目标模态的计算时间低于11.9 s,验证了所提方法的有效性。     

有限变工况特征迁移学习方法及其在高速列车轴箱 轴承故障诊断中的应用

以高速列车轴箱轴承为研究对象,提出了一种适用于有限数量变工况下的轴承故障诊断方法。该方法以有监督的学习模式构造自编码器,将不同工况下特征值集向参考工况下特征集做映射迁移,从而减弱由工况变化引起的轴承故障特征值改变的影响。再将迁移后的特征集输入由参考工况特征集预训练的基于卷积神经网络的故障诊断模型,实现变工况下轴承故障的诊断。凯斯西储大学轴承公开数据集和高速列车轴箱轴承数据集的试验结果表明,经监督式自编码器特征迁移后的轴承故障识别准确率有了较大提升,该方法能够较好的实现有限工况下的特征序列的迁移,解决工况变化带来的故障特征的畸变问题。     

基于局部投影降噪的低频轴承故障诊断

本文将局部投影降噪算法结合共振解调技术对低频轴承进行故障诊断。局部投影算法将时间序列先进相重构,在高维的相空间上采用局部投影的方法将相空间分解成正交的子空间,来分离时序中背景信号和噪场分量。综合局部投影降噪算法及共振解调技术两都的优点,对低频轴承进行了故障分析与诊断。     

An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis

A novel time–frequency analysis method called complementary complete ensemble empirical mode decomposition (EEMD) with adaptive noise (CCEEMDAN) is proposed to analyze nonstationary vibration signals. CCEEMDAN combines the advantages of improved EEMD with adaptive noise and complementary EEMD, and it improves decomposition performance by reducing reconstruction error and mitigating the effect of mode mixing. However, because white noise mixed in with the raw vibration signal covers the whole frequency bandwidth, each mode inevitably contains some mode noise, which can easily inundate the fault-related information. This paper proposes a time–frequency analysis method based on CCEEMDAN and minimum entropy deconvolution (MED) for fault detection of rolling element bearings. First, a raw signal is decomposed into a series of intrinsic mode functions (IMFs) by using the CCEEMDAN method. Then a sensitive parameter (SP) based on adjusted kurtosis and Pearson’s correlation coefficient is applied to select a sensitive mode that contains the most fault-related information. Finally, the MED is applied to enhance the fault-related impulses in the selected IMF. The fault signals of high-speed train axle-box bearing are applied to verify the effectiveness of the proposed method. Results show that the proposed method can effectively reveal axle-bearing defects’ fault information. The comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN. Further, we conducted comparisons that highlight the superiority of our proposed method over individual CCEEMDAN and MED methods and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.     

Sifting process of EMD and its application in rolling element bearing fault diagnosis

Among the vibration-based fault diagnosis methods for rolling element bearing, the shock pulse method (SPM) combined with the demodulation method is a useful quantitative technique for estimating bearing running state. However, direct demodulation often misestimates the shock value of characteristic defect frequency. To overcome this disadvantage, the vibration signal should be decomposed before demodulation. Empirical mode decomposition (EMD) can be an alternative for preprocess bearing fault signals. However, the trouble with this method’s application is that it is time-consuming. Therefore, a novel method that can improve the sifting process’s efficiency is proposed, in which only one time of cubic spline fitting is required in each sifting process. As a consequence, the time for EMD analysis can be evidently shortened and the decomposition results simultaneously maintained at a high precision. Simulations and experiments verify that the improved EMD method, combined with SPM and demodulation analysis, is efficient and accurate and can be effectively applied in engineering practice. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin Hongbo Dong was born in Chaoyang, China, in 1979. He received the B.E. and M.E. degree from Northwestern Polytechnical University in Mechanical Engineering in 2002 and 2005 respectively and received the Ph.D degree from Xi’an Jiaotong University in Mechanical Engineering in 2009. His research interests include fault diagnosis of rotor and bearing system. Bing Li was born in Xuzhou, China, in 1976. He received the B.E. and M.E. degree from Northwestern Polytechnical University in Mechanical Engineering in 1999 and 2002 respectively and received the Ph.D degree from Xi’an Jiaotong University in Mechanical Engineering in 2005. After graduating from Xi’an Jiaotong University, he works as a lecturer in Xi’an Jiaotong University. His research interests include wavelet finite element theory and its application in fault diagnosis.     

A fault diagnosis approach for roller bearing based on VPMCD under variable speed condition

Essentially the fault diagnosis of roller bearing is a process of pattern recognition. However, existing pattern recognition method failed to capitalize on the nature of multivariate associations between the extracted fault features. Targeting such limitation, a new pattern recognition method – variable predictive model based class discriminate (VPMCD) is introduced into roller bearing fault identification. The VPMCD consider that all or part of the feature values will exhibit interactions in nature and these associations will have different performances between different classes, which is always true in practice when faults occur in roller bearings. Target to the characteristics of non-stationary and amplitude-modulated and frequency-modulated (AM–FM) of vibration signal picked up under variable speed condition, a fault diagnosis method based upon the VPMCD, order tracking technique and local mean decomposition (LMD) is put forward and applied to the roller bearing fault identification. Firstly, LMD and order tracking analysis method are combined to extract the fault features of roller bearing vibration signals under variable speed condition; Secondly, the feature values are regard as the input of VPMCD classifier; finally, the working condition and fault patterns of the roller bearings are identified automatically by the output of VPMCD classifier. The analysis results from experimental signals with normal and defective roller bearings indicate that the proposed fault diagnosis approach can distinguish the roller bearing status-with or without fault and fault patterns under variable speed condition accurately and effectively.     

Adaptive combined HOEO based fault feature extraction method for rolling element bearing under variable speed condition

It is still a challenge for condition monitoring and fault diagnosis for rolling element bearings working under variable speed, while some conventional diagnostic methods are useless. Order tracking is commonly used as an effective tool of the non-stationary vibration analysis for rotating machinery and tacho-less order tracking may be more applicable for practical situations, while the key point is to obtain more accurate instantaneous rotating speed. What’s more, the collected bearing fault vibration signals always contain strong background noise that greatly affects the result of fault feature extraction. To solve these problems, a fault feature extraction method is proposed in this study. The Chirplet-based approach was used to estimate some obvious harmonics of instantaneous rotating speed and the average value of these components was regarded as the final instantaneous rotating speed to reduce the estimation error. Since the higher order energy operator (HOEO) can not only improve the signal-to-noise ratio and signal-to-interference ratio, but is also easily applied, an adaptive combined HOEO method based on hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC) was constructed to enhance the impulse components, and then, the fault features were extracted by the order spectrum analysis. Simulation and experimental results indicate that the proposed algorithm is effective for rolling element bearings’ fault diagnosis under variable speed condition.     

Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

Traditional artificial methods and intelligence-based methods of classifying and diagnosing various mechanical faults with high accuracy by extracting effective features from vibration data, such as support vector machines and back propagation neural networks, have been widely investigated. However, the problems of extracting features automatically without significantly increasing the demand for machinery expertise and maximizing accuracy without overcomplicating machine structure have to date remained unsolved. Therefore, a novel hierarchical learning rate adaptive deep convolution neural network based on an improved algorithm was proposed in this study, and its use to diagnose bearing faults and determine their severity was investigated. To test the effectiveness of the proposed method, an experiment was conducted with bearing-fault data samples obtained from a test rig. The method achieved a satisfactory performance in terms of both fault-pattern recognition and fault-size evaluation. In addition, comparison revealed that the improved algorithm is well suited to the fault-diagnosis model, and that the proposed method is superior to other existing methods.     

故障诊断中基于神经网络的特征提取方法研究

在电路状态检测与故障诊断过程中,恰当地选择特征参数是诊断成败的关键。本文研究了基于神经网络的特征评价和特征提取方法,利用神经网络的训练结果对特征参数进行合理的评价。由于神经网络满足高分辨率信息压缩所需的非线性映射条件,通过特征提取将电路故障模式识别中复杂的分类问题转移到特征处理阶段,利用神经网络有效地实现了特征参数的提取。诊断实例验证了该方法的有效性。     

滚动轴承数据库设计及其在故障诊断中的应用

针对国内外滚动轴承种类繁多、编号复杂以及轴承故障特征频率难以获得的现状,利用Power Builder强大的数据库功能,设计出一套数据完整、查询快捷方便,并与瑞典SKF公司的轴承故障特征频率参数相吻合的数据库系统,同时举例说明该系统可广泛应用于设备状态监测、故障诊断和预知维修领域。     

自适应Morlet小波降噪方法及在轴承故障特征提取中的应用

分析了Morlet小波变换的滤波特性及其时频分辨率,利用Morlet小波良好的时域和频域特性及奇异值分解技术,提出了一种基于自适应Morlet小波和SVD的降噪方法。针对滚动轴承故障在振动信号中表现为冲击衰减波形的特点,采用修正的Shannon熵方法同时优化Morlet小波的中心频率与带宽参数,实现其与冲击特征成分的最优匹配;针对根据小波系数矩阵奇异值曲线的过渡阶段求取最佳变换尺度的方法存在着不够快捷方便的不足,将其与小波系数奇异值比方法相结合来快速方便地求得最佳变换尺度;最后对信号进行降噪处理提取故障特征。对仿真信号和实际轴承内外圈故障信号的应用分析表明,该方法具有良好的降噪性能,能有效地提取出滚动轴承的微弱故障特征。     

时频分析在轴承故障诊断中的应用研究

在齿轮与滚动轴承的故障诊断中,由于振动信号的不平稳性,时频分析得到越来越广泛的重视.时频分析着重于研究信号能量在某一特定时间和频率处的分布.研究了一种新的时频分布,利用时频分布的新核来分析滚动轴承故障.通过对比证明,新核的能量集中度优于WVD.     

基于转速提取和优化MSB的滚动轴承故障诊断

在变转速工况下,齿轮箱滚动轴承的振动信号呈现强烈的非平稳性特征,导致无法对其进行故障诊断。针对这一问题,提出了一种基于转速提取和优化调制信号双谱(MSB)的滚动轴承故障诊断方法。首先,利用同步提取变换(SET),从原始振动信号中提取了参考轴的瞬时转速;再对原始信号进行了预白化和最小熵反褶积(MED)滤波,得到了特征增强的降噪信号,并结合提取的转速信号进行了角域重采样,建立了阶次域调制信号双谱(MSB);基于MSB分布,构造了更能体现主导调制分量与载波分量非线性耦合程度的改进载波谱;最后,根据改进载波谱对载波切片进行了择优挑选,结合MSB和双谱相干函数构造了改进调制谱,进一步消除了噪声的干扰,从而提取到了滚动轴承的显著故障特征。研究结果表明：该方法可以用于有效提取变转速齿轮箱滚动轴承的故障特征阶次,从而实现对滚动轴承进行有效的故障诊断;与传统的诊断方法相比,该方法具有明显的优势。     

A parameter-adaptive ACMD method based on particle swarm optimization algorithm for rolling bearing fault diagnosis under variable speed

Journal of Mechanical Science and Technology - Fault diagnosis for rolling bearing under variable speed is always a challenging topic since the vibration signal has time-varying characteristics. To...     

电机故障诊断专家系统及其应用

从电流诊断法原理分析入手,通过具体事例说明ENTEK公司的电机专家诊断系统对电机的故障诊断是十分有效的.     

重型装备故障诊断系统及其应用

研究了从设备自带控制系统、主动布置关键性能参数传感器、人机交互等三个途径为重型装备故障诊断系统获取故障信息的方法.随着诊断系统中故障信息量的由少到多的变化,分别采用故障部位结构检索方法、相似案例检索方法对故障进行诊断,以提高诊断准确性和诊断效率.通过在重型装备故障诊断中的应用,验证了所研究的诊断系统的有效性.     

第二代小波变换及其在故障诊断中的应用

第二代小波变换是一种基于提升原理的时域变换方法,介绍了第二代小波变换原理,给出了一种第二代小波变换过程中预测算子和提升算子的求取方法,在此基础上将第二代小波变换应用于矿用通风机的故障诊断中。结果表明该方法可以有效地分解信号和提取特征信息,在矿山机械故障诊断中具有良好的应用前景．     

主分量分析法及在故障诊断中的应用

2000年6月,日本著名设备诊断专家丰田利夫教授应邀在西安交大进行了一次学术交流和讲学,其中介绍了基于主分量分析方法(PCA)在故障诊断中的应用。由于它可以把多个评判参数进行综合优化,达到了多参量识别的目的,因而受到与会者的普遍欢迎。本文是特邀宋京伟教授依据此次讲学活动而写的补充材料。