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改进小波去噪Teager算子的齿轮微弱故障提取方法
Weak Fault Diagnosis Method of Gearbox Based on Improved Wavelet Denoising-Teager Energy Operator
 [PDF]

何巍,袁亮,章翔峰
- , 2018, DOI: 10.16450/j.cnki.issn.1004-6801.2018.24
Abstract: 针对齿轮箱在强噪声背景下齿轮微弱故障振动信号的特征不易被提取的问题,提出将改进小波去噪和Teager能量算子相结合的微弱故障特征提取方法。采用改进小波阈值函数对振动信号进行去噪处理,与形态学滤波和传统小波阈值函数相比能够有效地提高信号的信噪比。对去噪后的信号进行集合经验模态分解(ensemble empirical mode decomposition,简称EEMD)得到若干本征模式函数(intrinsic mode function,简称IMF),计算各IMF分量与原信号的相关系数并结合各IMF分量的频谱剔除虚假分量。对有效的IMF分量计算其Teager能量算子,并重构得到Teager能量谱,对重构信号进行时频分析并将其结果与原信号的希尔伯特黄变换(Hilbert Huang transform,简称HHT)得到的边际谱进行对比。实验研究结果表明,本研究方法相比HHT能够对齿轮微弱故障特征进行更为有效地提取,验证了本研究方法在齿轮箱微弱故障诊断中的可行性。
In order to solve the problem that the characteristic of the weak fault vibration signal of gearbox was not easy to be extracted in the strong noise background, a weak fault diagnosis method based on improved wavelet denoising pretreatment and Teager-kaiser energy operator is presented. The original signal is denoised by the method of wavelet improved threshold function; the signal-to-noise ratio is improved effectively compared to morphological filter method and traditional threshold function method. The denoised signal is composed into several intrinsic mode functions(IMFs) by ensemble empirical mode decomposition (EEMD). The correlation coefficients of each IMF component and the original signal are calculated, and the effective components are screened by combining the spectrum of each IMF component. A time-frequency analysis result of reconstruction signal that used the effective IMF components to get the reconstructed Teager energy spectrum, is compared with a marginal spectrum that used HHT to original signal. The comparison research results show that the proposed method is more effective to extract the week characteristics of gear fault. And the results also prove the proposed method worked.
基于DT-CWT自适应Teager能量谱的轴承早期故障诊断
Early Fault Diagnosis of Rolling Bearing Based on Dual-tree Complex Wavelet Transform Adaptive Teager Energy Spectrum
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任学平,王朝阁,张玉皓,王建国
- , 2017, DOI: 10.16450/j.cnki.issn.1004-6801.2017.04.016
Abstract: 针对滚动轴承早期故障特征信息难以识别以及带通滤波器参数设置依赖使用者经验等造成共振带不能有效确定并自适应提取的问题,提出了频带幅值熵的概念。在此基础上,将双树复小波变换和Teager能量谱结合,提出了基于双树复小波自适应Teager能量谱的早期故障诊断方法。首先,利用双树复小波将采集到的振动信号分解为不同频带的子信号,并计算各子带的频带幅值熵;然后,将熵值按升序排列后依次作为阈值,提取频带幅值熵大于阈值的子带,依据峭度指标确定最佳阈值,从而自适应并且有效地提取出共振带;最后,对共振带进行Teager能量谱分析,即可从中准确地识别出轴承的故障特征频率。通过信号仿真与实验数据分析验证了该方法的有效性。
Aiming at the early fault feature information of rolling bearings is difficult to identify, and the parameter setting of band-pass filter depends on the user experience, which makes the resonance frequency band not be effectively determined and extracted, the concept of amplitude entropy of frequency band is proposed. On this basis, the dual-tree complex wavelet transform and Teager energy spectrum are combined, and a rolling bearing early fault feature extraction method is proposed based on dual-tree complex wavelet transform adaptive Teager energy spectrum. Firstly, original fault signals are decomposed into several different frequency components through dual-tree complex wavelet decomposition, and the frequency amplitude entropy of each sub-band is calculated. Then the entropy are arranged in ascending order and in turn as a threshold to extract the entropy value greater than the threshold value of the sub bands. The optimal threshold is determined based on the kurtosis index, thus the resonance band is extracted adaptively and effectively. Finally, the fault characteristic frequency of the bearing could be accurately identified from the energy spectrum of the resonance band. The signal simulation and experimental data analyses verify the effectiveness of the proposed method.
基于HHT与TEO融合的谐振接地故障选线
Fault Line Selection for Resonance Grounding Based on the Combination Algorithm of HHT and TEO
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杨明, 杨鹏
Smart Grid (SG) , 2012, DOI: 10.12677/SG.2012.24022
Abstract: 本文提出了基于希尔伯特–黄变换(Hilbert-Huang Transform, HHT)与Teager能量算子(Teager Energy Operator, TEO)相融合的谐振接地选线新方法。首先对零序电流信号进行经验模态分解法(Empirical Mode De-composition, EMD)分解,然后将TEO合理的引入选线算法中,并与HHT极性判别法相融合,实现了HHT与TEO两者融合的优势互补。通过仿真分析,验证了所提方法的可行性和有效性。
The combination method of Hilbert-Huang Transform (HHT) and Teager Energy Operator (TEO) for fault line selection with resonance grounding is proposed in this paper. The zero sequence current signals are decomposed using Empirical Mode Decomposition (EMD). The TEO is reasonable introduced into algorithm of fault line selection. The TEO is combined with the method of HHT polarity estimate. The complementary advantage for combination method of HHT and TEO is implemented. The feasibility and effectiveness of the proposed method is verified by simulation results.
Study on Fault Detection of Rolling Element Bearing Based on Translation-Invariant Denoising and Hilbert-Huang Transform  [cached]
Lijia Xu
Journal of Computers , 2012, DOI: 10.4304/jcp.7.5.1142-1146
Abstract: In order to detect rolling element bearing faults from strong background noise, a new method based on translation-invariant denoising (TID) and hilbert-huang transform (HHT) is proposed. Firstly, the original vibration signals are preprocessed using TID to suppress abnormal interference of noise to improve the decomposition quality of HHT. Secondly, the denoised signals are decomposed into a set of intrinsic mode functions (IMFs) during empirical mode decomposition (EMD) process of HHT. Hilbert spectral analysis is further played on IMFs to capture the bearing defect frequencies. The performance of the proposed method is tested, and the experiment results show that this method can effectively extract the fault features of bearing and recognize the faults successfully. So the proposed method is a good-suited technique for bearing fault detection.
A Fault Diagnosis Approach for Roller Bearings Based on Hilbert-Huang Transform and AR Model
一种基于Hilbert-Huang变换和AR模型的滚动轴承故障诊断方法

CHENG Jun-sheng,YU De-jie,YANG Yu,
程军圣
,于德介,杨宇

系统工程理论与实践 , 2004,
Abstract: A fault diagnosis approach for roller bearings based on Hilbert-Huang transform and AR model is proposed. The Hilbert-Huang transform is used to decompose the vibration signal of a roller bearing into a number of IMF components and the instantaneous amplitudes and frequencies of each IMF component are obtained. Then the AR model of each instantaneous amplitude and frequency sequence is established. The main auto-regressive parameters and the variances of remnant are regarded as the feature vectors. Thus, the Mahalanobis distance criterion function is established to identify the condition and fault pattern of a roller bearing. Practical examples demonstrate that the approach based on Hilbert-Huang transform and AR model can be applied to the roller bearing fault diagnosis effectively.
基于Hilbert-Huang变换的列车车轮失圆故障诊断
The Fault Diagnosis Method of Railway Out-of-Round Wheels Using Hilbert Huang Transform
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李奕璠,刘建新,李忠继
- , 2016, DOI: 10.16450/j.cnki.issn.1004-6801.2016.04.019
Abstract: 研究列车车轮失圆的检测与诊断问题,采用基于改进的希尔伯特-黄变换(Hilbert-Huang transform, 简称HHT)的处理方法,首先,针对HHT方法固有的模态混叠现象,提出一种形态滤波-能量原则算法;然后,建立车辆轨道耦合动力学模型和典型的车轮故障模型,计算轴箱垂向振动的动态响应;最后,运用改进的HHT分析方法提取正常车轮、多边形化车轮和擦伤车轮引起的轴箱垂向振动的特征。研究结果表明,正常车轮与故障车轮之间以及不同类型故障的车轮之间Hilbert谱差异显著,可见该方法能够有效诊断车轮失圆故障。
In order to study the detection and diagnosis problem of railway out-of-round wheels, a method based on improved Hilbert-Huang transform(HHT) was utilized. First, in light of the mode mixing phenomenon of HHT, a novel morphology filtering and energy principle algorithm was put forward to eliminate mode mixing. Then, the vehicle track coupling dynamics model and representative wheel fault models were set up to calculate the vertical vibration dynamic response of the axle box. Finally, the axle box vertical vibration characteristic induced by a normal wheel, polygonal wheel and flat wheel were studied with revised HHT. The research results show that there was a significant difference in the Hilbert spectrum between healthy wheels and fault wheels, as well as fault wheels with different fault types. This method can thus effectively diagnose faults of out-of-round wheels.
Classification of fault diagnosis in a gear wheel by used probabilistic neural network, fast Fourier transform and principal component analysis
Piotr CZECH
Transport Problems : an International Scientific Journal , 2007,
Abstract: This paper presents the results of an experimental application of artificial neural network as a classifier of the degree of cracking of a tooth root in a gear wheel. The neural classifier was based on the artificial neural network of Probabilistic Neural Network type (PNN). The input data for the classifier was in a form of matrix composedof statistical measures, obtained from fast Fourier transform (FFT) and principal component analysis (PCA). The identified model of toothed gear transmission, operating in a circulating power system, served for generation of the teaching and testing set applied for the experiment.
高速齿轮动态传动误差的希尔伯特-黄变换分析
Analyzing Dynamic Transmission Errors of a High-speed Gear with the Hilbert-Huang Transform
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刘洋,唐进元,钱露露
- , 2017,
Abstract: 高速齿轮传动误差测量与分析是一个急需解决的难题。通过构建动态传动误差测量系统,测得高速齿轮动态传动误差曲线,并将希尔伯特-黄变换(HHT)应用于高速齿轮动态传动误差信号分析,自适应得到高速齿轮动态传动误差信号不同频率成分的固有模态函数,分离出轴频分量和齿频分量,获得动态传动误差希尔伯特谱。为了验证HHT的可行性与正确性,利用快速傅立叶变换(FFT)对相同的传动误差信号进行频谱分析。结果表明:HHT在高速齿轮动态传动误差信号的分频、滤波方面都具有优越性;相比FFT,希尔伯特-黄变换能够很好的提取信号的高频特征,将传动误差信号各成分分离,分析结果更加精确。
It is urgently necessary to measure and analyze the dynamic transmission error signals of a high-speed gear. The paper constructs a dynamic transmission error measurement system, from which the error curve is obtained and then the error curve is analyzed with the Hilbert-Huang Transform(HHT). The intrinsic mode functions of different frequency components of the dynamic transmission error signal is adaptively gained. Then we separate the axis frequency spectrum and tooth frequency spectrum and obtain the Hilbert spectrum of the error signal. To verify the feasibility and correctness of the error analysis method based on the HHT, we use the Fast Fourier Transform (FFT) to analyze the same transmission error signal. The analysis results show that the HHT is good at frequency spectrum division and filtering of the dynamic transmission error signal of high-speed gear. Compared with the FFT, it can accurately separate the frequency spectra of the transmission error signal, thus having more accurate analysis
Fault diagnosis in gear using wavelet envelope power spectrum
M Lokesha, MC Majumder, KP Ramachandran, KFA Raheem
International Journal of Engineering, Science and Technology , 2011,
Abstract: In recent years, improvement has been achieved in vibration signal processing, using wavelet analysis for condition monitoring and fault diagnosis. The use of wavelet analysis has proven to be efficient to detect faults in vibration signals with nonstationary, transient characteristics/components. An experimental data set is used to compare the diagnostic capability of the fast Fourier transform power spectrum to the wavelet envelope power spectrum as respectively computed using Laplace and Morlet wavelet functions. The gear testing apparatus was used for experimental studies to obtain the vibration signal from a healthy gear and a faulty gear.
Detection and Localization of Tooth Breakage Fault on Wind Turbine Planetary Gear System considering Gear Manufacturing Errors  [PDF]
Y. Gui,Q. K. Han,Z. Li,F. L. Chu
Shock and Vibration , 2014, DOI: 10.1155/2014/692347
Abstract: Sidebands of vibration spectrum are sensitive to the fault degree and have been proved to be useful for tooth fault detection and localization. However, the amplitude and frequency modulation due to manufacturing errors (which are inevitable in actual planetary gear system) lead to much more complex sidebands. Thus, in the paper, a lumped parameter model for a typical planetary gear system with various types of errors is established. In the model, the influences of tooth faults on time-varying mesh stiffness and tooth impact force are derived analytically. Numerical methods are then utilized to obtain the response spectra of the system with tooth faults with and without errors. Three system components (including sun, planet, and ring gears) with tooth faults are considered in the discussion, respectively. Through detailed comparisons of spectral sidebands, fault characteristic frequencies of the system are acquired. Dynamic experiments on a planetary gear-box test rig are carried out to verify the simulation results and these results are of great significances for the detection and localization of tooth faults in wind turbines. 1. Introduction Planetary gear systems have been widely used in wind power systems because of the advantages of compact structure, large carrying capacity, and high transmission efficiency [1]. In recent years, the tooth faults that have occurred in planetary gear systems have brought numerous troubles to wind power plants [2–4]. Therefore, fault diagnosis of the planetary gear system is of great significant to the safe operation of wind turbine. Tooth pitting, spalling, cracking, and breakage are some of the common fault modes that have occurred in wind turbine planetary gear systems. Detection and localization of those faults are of great significance. Vibration based diagnosis is one of the most effective and frequently used health monitoring technologies. In recent years, much attention has been paid on analyzing the vibration signal in time and frequency domains. To name a few, Lei et al. [5] extracted two diagnostic parameters based on the examination of vibration characteristics of a planetary gearbox in both time and frequency domains. Experiments revealed that the proposed diagnostic parameters performed better than other parameters. Lin and Zuo [6] introduced a method based on the Morlet wavelet time-frequency analysis. The method was found more effective in detection of tooth cracks than two other types of discrete wavelet transform. Liu et al. [7] carried out fault diagnosis based on the local mean decomposition (LMD)
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