基于形态小波与排列熵的变速器齿轮故障识别方法

在深入研究形态小波与排列熵的基础上,提出一种新的变速器齿轮故障识别方法。引入形态小波的概念,提出采用形态Haar小波对实测变速器齿轮振动信号进行降噪预处理;将排列熵作为变速器齿轮故障的特征值,提取了包括齿轮正常、齿面轻度磨损、齿面中度磨损和断齿等4种工况的振动信号;依据不同的故障对应不同的排列熵分布,对各种故障状态进行分类,同时对比了未降噪信号的排列熵分布。变速器齿轮故障识别的实例验证了形态小波与排列熵结合能有效提高齿轮故障分类能力。     

基于样本熵的变速器微弱异响故障特征提取研究

针对某变速器新产品在特定挡位转速区间存在的微弱异响,运用非线性动力学参数样本熵提取变速器在不同挡位下的运行特征;通过实例分析了不同参数对样本熵提取齿轮故障的影响,选择了合适的参数计算样本熵;在此参数下研究了正常挡位与故障挡位样本熵和近似熵随转速变化情况。研究表明,输入轴转速升高,正常挡位样本熵和近似熵变化不大,而故障挡位样本熵和近似熵减小,且样本熵相比近似熵可更有效提取变速器齿轮特征。并针对样本熵异常的某变速器4挡齿轮故障进行了阶次包络分析,实现了变速器齿轮故障的判别。     

基于循环多核相关熵的故障检测方法及应用* .txt

摘要：针对传统基于二阶统计量的循环平稳信号处理方法不能有效处理脉冲噪声干扰的问题,提出了一种基于循环多核相关熵的故障检测方法。首先,给出了多核相关熵的定义,推导了循环多核相关熵函数和循环多核相关熵谱密度函数计算公式,分析了循环多核相关熵的降噪机理;其次,用仿真信号验证了在低噪声比（SNR=-5）情况下循环多核相关熵的降噪性能,表明了循环多核相关熵不仅能有效抑制高斯噪声,而且能有效抑制非高斯噪声,循环多核相关熵为高斯、非高斯噪声的处理提供了一种鲁棒性处理方法;最后,将循环多核相关熵方法应用于齿轮箱齿轮齿面磨损故障诊断,实验结果表明：循环多核相关熵具有解调功能,能准确刻画齿轮齿面磨损故障的频谱特征,可有效提取淹没在强噪声环境中的微弱信号,提高了信噪比,证明了此方法为一种齿轮故障诊断的有效方法。 .txt     

基于循环多核相关熵的故障检测方法及应用

针对传统基于二阶统计量的循环平稳信号处理方法不能有效处理脉冲噪声干扰的问题,提出了一种基于循环多核相关熵的故障检测方法。首先,给出了多核相关熵的定义,推导了循环多核相关熵函数和循环多核相关熵谱密度函数计算公式,分析了循环多核相关熵的降噪机理;其次,用仿真信号验证了在低噪声比(SNR=-5)情况下循环多核相关熵的降噪性能,表明了循环多核相关熵不仅能有效抑制高斯噪声,而且能有效抑制非高斯噪声,循环多核相关熵为高斯、非高斯噪声的处理提供了一种鲁棒性处理方法;最后,将循环多核相关熵方法应用于齿轮箱齿轮齿面磨损故障诊断,实验结果表明:循环多核相关熵具有解调功能,能准确刻画齿轮齿面磨损故障的频谱特征,可有效提取淹没在强噪声环境中的微弱信号,提高了信噪比,证明了此方法为一种齿轮故障诊断的有效方法。     

全矢LMD能量熵在齿轮故障特征提取中的应用

齿轮故障信号具有非线性、非平稳特征,齿轮发生故障时,信号的能量结构随之改变,在不同的频带内能量不同。传统方法采用局部均值分解(LMD)提取振动信号的能量熵,将能量熵指标作为故障评判标准进行故障分类,依靠单一传感器信息源进行故障诊断,因而容易造成误诊、漏诊。全矢LMD能量熵法融合了双通道同源信息的回转能量,可降低故障误判率。通过实验模拟齿轮正常、齿根裂纹、断齿、缺齿等4种状态,验证了全矢LMD能量熵作为故障特征能达到很好的故障分类效果。     

基于多维度模糊熵的齿轮故障诊断方法

针对齿轮的故障诊断问题,引入模糊熵的方法对齿轮振动信号进行分析。通过研究嵌入维数和延迟时间对信号模糊熵的影响,提出多维度模糊熵的齿轮故障特征提取方法。利用多维度模糊熵特征提取方法提取故障特征,并结合支持向量机建立了齿轮故障诊断模型。对实测齿轮故障数据进行分析,证明了多维度模糊熵方法可以有效提取齿轮不同状态的特征信息,与支持向量机结合可以精确地诊断齿轮典型故障,具有一定的优势。     

基于DLMD样本熵和模糊聚类的滚动轴承故障诊断

针对传统的局部均值分解(LMD)方法不能有效提取微弱高频信号成分的问题,提出了一种基于微分的微分局部均值分解(DLMD)方法,在此基础上,将DLMD、样本熵和模糊聚类分析相结合,提出了一种基于DLMD样本熵和模糊聚类的滚动轴承故障诊断方法。该方法首先对滚动轴承振动信号进行微分局部均值分解,得到若干具有物理意义的乘积函数(PF)分量,然后求取各PF分量的样本熵并将其作为特征向量,最后通过模糊聚类对特征向量进行识别分类。实验结果表明,基于DLMD样本熵和模糊聚类相结合的方法能够准确、有效地对滚动轴承故障信号进行识别分类。     

基于小波包分析的桥式起重机减速器齿轮故障诊断

齿轮故障是影响减速器正常运行的关键问题。采用声发射检测手段,针对减速器齿轮中正常、齿根裂纹、齿面磨损及断齿四种状态进行分析,提取减速器齿轮工作过程中的声发射信号进行诊断。采用基于最小模糊隶属度的信息融合方法诊断减速器齿轮故障。实验结果表明,诊断结果的准确率较高,表明此方法可实现对减速器齿轮故障状态的实时精确诊断,研究结果为及时发现和更换失效齿轮,对提高减速器的工作效率和使用寿命等方面具有重要意义。     

基于EEMD、模糊熵和SVM的齿轮故障诊断方法

齿轮故障信号具有不平稳特性,故障信号特征向量难提取,典型的齿轮故障数据样本少。针对这些问题,提出基于总体平均经验模式分解(EEMD)、模糊熵和支持向量机(SVM)相结合的诊断方法。首先通过传感器采集得到加速度信号,然后,通过EEMD降低模态混叠,并将加速度信号分解成多个稳定的本征模态函数信号(IMFs)。其次,利用模糊熵能够表现信号复杂程度并且稳定的性质,取多个稳定IMFs的前几项计算模糊熵。因为SVM能够在小样本集情况下建立决策规则,所以将IMFs的前几项模糊熵值作为特征向量输入SVM训练。最后,SVM算法与常用神经网络比较,对样本训练、测试并诊断故障,说明SVM算法优于神经网络。齿轮故障诊断实验结果表明,所提出的方法诊断准确率达92.5%,可实现齿轮故障信息提取和齿轮故障的有效诊断。     

齿面微观几何分析技术在齿轮优化设计中的应用

在纵置变速器中,常啮合齿轮副可靠性的高低对于变速器整体性能水平有很大的影响.针对某汽车纵置变速器常啮合齿轮失效故障进行分析,根据分析结果,运用RomaxDesigner软件中的齿面微观几何分析技术对该齿轮副进行优化设计.对齿轮副进行齿面微观优化修形,并对优化修形前后齿面载荷分布均匀性、齿面接触和齿根弯曲强度安全系数等参数进行对比.对比结果表明,通过优化修形可以使齿面载荷分布更加均匀,提高齿面接触和齿根弯曲强度,进而达到增加齿轮寿命的目的.     

Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS

The small size, low weight, and large transmission ratio of planetary gear have resulted in large-scale use, low speed, and heavy-duty mechanical systems. Poor working conditions of planetary gear lead to frequent occurrence of faults. A method is proposed for diagnosing faults in planetary gear based on fuzzy entropy of Local mean decomposition (LMD) and Adaptive neuro-fuzzy inference system (ANFIS). The original vibration signal is decomposed into six Product function (PF) components and a residual using LMD. Given that decomposed PF components contain the main fault feature information, fuzzy entropy is used to reflect the complexity and irregularity of each PF component. The fuzzy entropies of each PF component are defined as the input of the ANFIS model, and its parameters and membership functions are adaptively adjusted based on training samples. Finally, fuzzy inference rules are determined, and the optimal ANFIS model is obtained. Testing samples are used to verity the trained ANFIS model. The overall fault recognition rate reaches 88.8%, and the fault recognition rate for gear with wear reaches 96%. Therefore, the proposed method is effective at diagnosing planetary gear faults.     

基于MCKD和改进HHT多尺度模糊熵的齿轮故障诊断方法

针对齿轮故障信号常伴有大量噪声,故障特征难以提取的问题,提出一种基于最大相关峭度解卷积(MCKD)和改进希尔伯特-黄变换(HHT)多尺度模糊熵的故障诊断方法。首先采用MCKD算法对采集到的齿轮振动信号进行降噪处理,以提高信号的信噪比;然后利用自适应白噪声完备经验模态分解(CEEMDAN)对降噪后信号进行分解,获得一系列不同尺度的固有模态函数(IMF),并通过相关系数-能量的虚假IMF评价方法选取对故障敏感的模态分量;最后计算敏感IMF分量的模糊熵,将获得的原信号多尺度的模糊熵作为状态特征参数输入最小二乘支持向量机(LS-SVM)中,对齿轮的故障类型进行诊断。实测信号的诊断结果表明,该方法可实现齿轮故障的有效诊断。     

基于谱熵的齿轮故障诊断方法研究

齿轮故障诊断中,采用何种有效的方法对随机动态信号进行分析和特征提取是关键所在。在实际工程当中所采集到的系统信号不可避免地受到噪声的污染,所以普通的一些处理方法如功率谱分析法等,对噪声的存在很敏感,检测分析结果往往不很理想,且很难准确区分故障。谱熵方法从统计学理论入手,反映了信号的无序性,对噪声具有一定的鲁棒性。本文将谱熵理论引入到机械齿轮传动系统中,对齿轮发生的裂纹、磨损故障进行了特征提取、区分与诊断,并与正常齿轮进行了对比,分析模拟和实验结果表明,效果良好,识别诊断的精度在90%以上,为机械齿轮传动系统的故障识别与诊断提供了一种有效方法。     

基于小波变换-模糊聚类的变速箱齿轮故障诊断

在对车辆变速箱齿轮振动加速度信号进行小波变换的基础上，提出了基于尺度-能量谱的特征提取和模糊聚类相结合的车辆变速箱齿轮故障诊断方法。该方法应用于LC5T81变速箱齿轮的故障诊断中，能够比较准确地识别与诊断出变速箱齿轮的走合运行状态、磨损运行状态和故障运行状态，具有一定的实用价值。     

基于改进LMD和IED-SampEn的齿轮故障特征提取方法

提出基于改进的局部均值分解(Local mean decomposition,LMD)和瞬时能量分布(Instantaneous energy distribution,IED)-样本熵(Sample entropy,Samp En)的齿轮故障特征提取方法。针对LMD存在的端点效应问题,提出最大相似系数法改进的LMD方法,该方法通过在信号内部寻找与两端指定波段相似系数最大的波段,来实现端点效应的改善。进行仿真验证,结果表明该方法能有效改善LMD的端点效应问题。采用改进的LMD方法分解信号得到瞬时幅值函数,由此可以获得信号的瞬时能量分布,将其作为样本熵输入获得IED-Samp En,通过试验研究并与PF-Samp En进行对比,结果表明IED-Samp En能够合理地、有效地反应齿轮的故障状态,作为齿轮振动信号的特征矢量具有典型性,可以作为一种有效的故障特征。     

Study on planetary gear fault diagnosis based on entropy feature fusion of ensemble empirical mode decomposition

Because planetary gear is characterized by its small size, light weight and large transmission ratio, it is widely used in large-scale, low-speed and heavy-duty mechanical systems. Therefore, the fault diagnosis of planetary gear is a key to ensure the safe and reliable operation of such mechanical equipment. A fault diagnosis method of planetary gear based on the entropy feature fusion of ensemble empirical mode decomposition (EEMD) is proposed. The intrinsic mode functions (IMFs) with small modal aliasing are obtained by EEMD, and the original feature set is composed of various entropy features of each IMF. To address the insensitive features in the original feature set and the excessive feature dimension, kernel principal component analysis (KPCA) is used to process the original feature set. Kernel principal component extraction and feature dimension reduction are performed. The fault diagnosis of planetary gear is eventually realized by applying the extracted kernel principal components and learning vector quantization (LVQ) neural network. The experiments under different operation conditions are carried out, and the experimental results indicate that the proposed method is capable of extracting the sensitive features and recognizing the fault statuses. The overall recognition rate reaches to 96% when the motor output frequency is 45 Hz and the load is 13.5 N m, and the fault recognition rates of the normal gear, the gear with one missing tooth and the broken gear can reach to 100%. The recognition rates of different fault gears under other operation conditions also can achieve better results. Thus, the proposed method is effective for the diagnosis of planetary gear faults.     

一种基于模糊故障树的设备运行风险分析方法

针对传统故障树方法的缺．最，提出一种基于模糊故障树设备运行风险分析方法。该方法使用参照函数为正态型的L—R模糊数来描述设备故障树中各事件发生的概率，根据模糊数的运算法则和模糊算子AND和OR的计算方法，推导出顶事件发生的模糊概率及其隶属函数分布。用一种行星轮传动系统作为案例，验证了该传动系统的安全性，并为提高系统可靠性提供结构改进设计的理论依据。     

Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis

When faults occur in the gear, energy distribution of gear vibration signals measured in time–frequency plane would be different from the distribution under the normal state. Therefore, it is possible to detect a fault by comparing the energy distribution of gear vibration signals with and without fault conditions. Hilbert–Huang transform can offer a complete and accurate energy–frequency–time distribution. On the other hand, Shannon entropy could give a useful criterion for analyzing and comparing probability distribution and offer a measure of the information of any distribution. Targeting the feature of energy distribution of gear vibration signal, the merit of entropy and Hilbert–Huang transform, the concept of time–frequency entropy based on Hilbert–Huang transform is defined and furthermore gear fault diagnosis method based on time–frequency entropy is proposed. The analysis results from simulated signals and experimental signals with normal and defective gears show that the diagnosis approach proposed could identify gear status-with or without fault accurately and effectively. However, further study is needed to the classify gear fault pattern such as crack fault or broken teeth.     

Fault identification method for planetary gear based on DT-CWT threshold denoising and LE

Planetary gear is widely used in large-scale complex mechanical systems. However, because of the particularity of planetary gear transmission, serious wear and fatigue crack failures often occur in the sun gear, planet gears, and inner gear ring. In addition, every type of fault will experience different degradation processes. Improving the operation reliability of mechanical equipment through fault diagnosis of planetary gears and monitoring their degradation process is beneficial. This paper proposes a planetary gear fault identification method based on Dual-tree Complex wavelet transform (DT-CWT) threshold denoising and Laplacian eigenmaps (LE). The noise reduction processing of the original signal is achieved by the DT-CWT threshold denoising method, which takes full advantage of DT-CWT and is combined with the wavelet threshold of rigrsure principle. The original high-dimensional feature set, including the time domain features, frequency domain features, permutation entropy, and fractal box dimension of the denoised signal, is constructed from multi-angles. To solve the problems of excessively large feature dimension and the existence of redundant information, the LE algorithm is used to reduce the dimension of the original high-dimensional feature set, and the low-dimensional sensitive features are obtained. Through the above method, the effective identification of different fault states and different degradation states of the planetary gear are achieved.     

基于模糊故障树的液压系统故障诊断方法研究与应用

针对液压系统故障的多发性 ,故障在封闭回路中的传递性、设备构造的复杂性等特点 ,通过对现有故障诊断方法的分析 ,提出了基于模糊故障树理论的故障诊断方法。以印刷切纸机液压系统为研究对象 ,通过分析该系统的故障形式 ,建立了该系统的模糊故障树 ,进行了量化分析和算法研究 ,研究表明 ,这种方法较传统方法实用、有效。