基于信号共振稀疏分解与包络谱的齿轮故障诊断

当齿轮出现断齿、裂纹等局部故障时,其振动信号会出现周期性冲击脉冲。在齿轮故障早期,由于冲击脉冲微弱,常淹没在齿轮的啮合频率、转频等谐波成分以及噪声中,因此,对于齿轮早期故障,直接对齿轮振动信号做包络谱分析以诊断齿轮局部故障通常效果不佳。针对这一问题,将信号共振稀疏分解方法与包络谱分析相结合,提出了基于信号共振稀疏分解与包络谱的齿轮故障诊断方法。该方法采用信号共振稀疏分解将冲击脉冲从齿轮振动信号中分离出来,然后对冲击脉冲做Hilbert包络分析,获取冲击脉冲出现的周期,进而对齿轮状态和故障进行识别。仿真算例和应用实例证明了该方法的有效性。     

基于形态分量分析与能量算子解调的齿轮箱复合故障诊断方法

针对齿轮箱复合故障的故障特征分离,提出了一种基于形态分量分析与能量算子解调的齿轮箱复合故障诊断方法。该方法先根据振动信号中各组成成分形态的差异,采用形态分量分析方法构建不同形态的稀疏表示字典进行故障成分分离,将齿轮箱复合故障信号分解为包含齿轮故障信息的谐振分量、包含轴承故障信息的冲击分量和噪声分量,然后分别对谐振分量和冲击分量进行能量算子解调分析,最后根据各解调谱诊断齿轮和轴承故障。算法仿真和应用实例表明该方法能有效地分离齿轮箱复合故障振动信号中齿轮与轴承的故障特征。     

基于形态分量分析和包络谱的轴承故障诊断

滚动轴承出现局部损伤时,其振动信号往往由包含轴承自身振动的谐振分量、包含轴承故障信息的冲击分量及随机噪声分量构成。提出了基于形态分量分析和包络谱的滚动轴承故障诊断方法。该方法根据轴承振动信号中各组成成分的形态差异,利用改进的形态分量分析对滚动轴承故障振动信号中的谐振分量、冲击分量和噪声分量进行分离,然后对冲击分量进行Hilbert包络解调分析,根据包络谱诊断滚动轴承故障。算法仿真和应用实例表明,该方法能有效提取滚动轴承故障特征。     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.     

Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance

In the gear fault diagnosis, the emergence of periodic impulse components in vibration signals is an important symptom of gear failure. However, heavy background noise makes it difficult to extract the weak periodic impulse features. Therefore, the paper presents an impact fault detection method of gearbox by combining variational mode decomposition (VMD) with coupled underdamped stochastic resonance (CUSR) to extract the periodic impulse features. First, the adaptive VMD is presented to decompose the vibration signal into several intrinsic mode functions (IMFs), which can automatically determine the appropriate mode number according to the correlation kurtosis (CK) of decomposition results and extract the sensitive IMF component containing the main fault information. Next, the adaptive CUSR method is developed to analyze the selected sensitive IMF component, and the optimal system parameters are obtained by the genetic algorithm using the CK index as optimization objective function. Finally, the periodic impulse features are extracted by the output signal of CUSR system accurately. Experiments and engineering application verify the effectiveness and superiority of the proposed adaptive VMD-CUSR method for extracting the periodic impulse features in gear fault diagnosis compared to other methods.     

Detection of faults in gearboxes using acoustic emission signal

Vibration analysis is widely used in machinery diagnosis, and wavelet transform and envelope analysis have also been implemented in many applications to monitor machinery condition. Envelope analysis is well known as a useful tool for the detection of rolling element bearing faults, and wavelet transform is used in research to detect faults in gearboxes. These are applied for the development of the condition monitoring system for early detection of the faults generated in several key components of machinery. Early detection of the faults is a very important factor for condition monitoring and a basic component to extend CBM (Condition-Based Maintenance) to PM (Prediction Maintenance). The AE (acoustic emission) sensor has a specific characteristic on the high sensitivity of the signal, high frequency and low energy. Recently, AE technique has been applied in some studies for the early detection of machine fault. In this paper, a signal processing method for AE signal by envelope analysis with discrete wavelet transforms is proposed. Through the 15 days test using AE sensor, misalignment and bearing faults were observed and early fault stage was detected. Also, in order to find the advantage of the proposed signal processing method, the result was compared to that of the traditional envelope analysis and the accelerometer signal.     

基于信号共振稀疏分解和最大相关峭度解卷积的齿轮箱故障诊断

当齿轮箱内旋转零件发生故障时,其振动信号中的故障脉冲成分易被箱体中其他旋转部件的谐波信号和背景噪声所淹没,故障特征难以被有效提取。针对这一问题,提出了基于信号共振稀疏分解和最大相关峭度解卷积的故障诊断方法。该方法首先通过信号共振稀疏分解将信号中的低共振冲击成分从谐波分量和噪声中分离,然后对低共振分量进行最大相关峭度解卷积计算,进一步突出低共振分量中的周期脉冲成分,最后通过包络谱分析进行故障诊断。算法仿真、实验分析和工程应用结果表明,该方法能够有效提取强噪声信号中的周期性冲击成分,凸显故障特征,从而提供准确可靠的诊断结果。     

OEEMD与Teager能量算子结合的轴承故障诊断

针对滚动轴承发生局部故障时振动信号中微弱周期性冲击的特征提取问题,提出参数优化集合经验模式分解(optimal ensemble empirical mode decomposition,简称OEEMD)与Teager能量算子解调结合的滚动轴承故障诊断方法。首先,针对集合经验模式分解(ensemble empirical mode decomposition,简称EEMD)过程中两个关键参数k(加入白噪声的幅值系数)和m(集合平均次数)的准确选取问题,通过引入相关系数、相关均方根误差和信噪比分析,给出一种可自适应确定这两个参数取值的OEEMD方法,通过OEEMD将冲击从滚动轴承振动信号中分离出来;其次,采用Teager能量算子对其进行包络解调,计算出瞬时幅值后再对瞬时幅值进行包络谱分析,以获取冲击的特征频率,从而对滚动轴承故障进行准确诊断。仿真信号分析和应用实例验证了该方法的有效性。     

A new fault diagnosis algorithm for helical gears rotating at low speed using an optical encoder

Helical gears are widely used in gearboxes due to its low noise and high load carrying capacity, but it is difficult to diagnose their early faults based on the signals produced by condition monitoring systems, particularly when the gears rotate at low speed. In this paper, a new concept of Root Mean Square (RMS) value calculation using angle domain signals within small angular ranges is proposed. With this concept, a new diagnosis algorithm based on the time pulses of an encoder is developed to overcome the difficulty of fault diagnosis for helical gears at low rotational speeds. In this proposed algorithm, both acceleration signals and encoder impulse signal are acquired at the same time. The sampling rate and data length in angular domain are determined based on the rotational speed and size of the gear. The vibration signals in angular domain are obtained by re-sampling the vibration signal of the gear in the time domain according to the encoder pulse signal. The fault features of the helical gear at low rotational speed are then obtained with reference to the RMS values in small angular ranges and the order tracking spectrum following the Angular Domain Synchronous Average processing (ADSA). The new algorithm is not only able to reduce the noise and improves the signal to noise ratio by the ADSA method, but also extracts the features of helical gear fault from the meshing position of the faulty gear teeth, hence overcoming the difficulty of fault diagnosis of helical gears rotating at low speed. The experimental results have shown that the new algorithm is more effective than traditional diagnosis methods. The paper concludes that the proposed helical gear fault diagnosis method based on time pulses of encoder algorithm provides a new means of helical gear fault detection and diagnosis.     

Fault diagnosis of rolling element bearing using a new optimal scale morphology analysis method

Periodic transient impulses are key indicators of rolling element bearing defects. Efficient acquisition of impact impulses concerned with the defects is of much concern to the precise detection of bearing defects. However, transient features of rolling element bearing are generally immersed in stochastic noise and harmonic interference. Therefore, in this paper, a new optimal scale morphology analysis method, named adaptive multiscale combination morphological filter-hat transform (AMCMFH), is proposed for rolling element bearing fault diagnosis, which can both reduce stochastic noise and reserve signal details. In this method, firstly, an adaptive selection strategy based on the feature energy factor (FEF) is introduced to determine the optimal structuring element (SE) scale of multiscale combination morphological filter-hat transform (MCMFH). Subsequently, MCMFH containing the optimal SE scale is applied to obtain the impulse components from the bearing vibration signal. Finally, fault types of bearing are confirmed by extracting the defective frequency from envelope spectrum of the impulse components. The validity of the proposed method is verified through the simulated analysis and bearing vibration data derived from the laboratory bench. Results indicate that the proposed method has a good capability to recognize localized faults appeared on rolling element bearing from vibration signal. The study supplies a novel technique for the detection of faulty bearing.     

基于IEWT和MCKD的滚动轴承故障诊断方法

针对经验小波变换（Empirical wavelet transform,EWT）对强噪声环境中滚动轴承微弱故障诊断的不足,主要是傅里叶频谱分段不当的问题。提出一种基于最大相关峭度解卷积（Maximum correlated kurtosis deconvolution,MCKD）降噪与改进EWT相结合的滚动轴承早期故障识别方法。首先采用最大相关峭度解卷积算法以包络谱的相关峭度最大化为目标对原信号进行降噪处理、检测信号中的周期性冲击成分,然后根据信号Fourier频谱的包络极大值进行分段,通过分析各频段平方包络谱中明显的频率成分来诊断故障。新方法能有效降噪、增强信号中周期性冲击特征、降低单次偶然冲击的影响、抑制非冲击成分。通过对含外圈、内圈故障的滚动轴承进行试验分析,结果表明,相比于快速谱峭度图和小波包络分析方法,该方法提取出的特征更加明显,能有效实现滚动轴承早期微弱故障的识别。     

基于LMD的包络谱特征值在齿轮箱故障诊断中的应用研究

针对齿轮箱故障振动信号大多是多分量的调幅-调频信号,而传统包络分析法又太依赖经验值选取参数的问题,对齿轮箱振动信号的分解方法、包络分析方法以及提取特征值等方面进行了研究,提出了一种基于局部均值分解(local mean de-composition,LMD)的包络谱特征值的方法。该方法首先利用局部均值分解对齿轮箱信号进行了处理,获得了包含有不同频率特征的PF(product function)分量,最后对包含有主要故障信息的第一级PF分量进行了包络分析,提取了包络谱的特征频率,以此来判别齿轮箱的工作状态和故障类型。利用齿轮箱正常状态、局部损伤、磨损故障3种齿轮箱振动信号的实例进行了验证。研究结果表明,利用LMD分解后求取包络谱特征频率的方法能够较为准确地判别齿轮箱的工作状态和故障类型。     

基于包络加窗同步平均的行星齿轮箱特征提取

行星齿轮箱由于行星轮通过效应、太阳轮与行星架的旋转及时变工况,导致其振动响应存在时变传递路径及非平稳性等特点,且传统的同步平均将不能直接应用于行星齿轮箱。笔者在国外加窗同步平均的基础上提出一种能有效克服时变传递路径及非平稳性的基于包络信号角域加窗同步平均的行星齿轮箱故障特征提取方法。首先,基于谱峭度提取出行星齿轮箱振动信号的包络信号;其次,再利用计算阶比跟踪技术对包络信号进行等角度重采样,行星架每旋转一圈,选择合适的窗函数对角域信号进行多齿宽加窗截取;最后,验证齿轮啮合齿序特征,根据重排齿序对加窗信号进行重构振动分离信号,对振动分离信号进行角域同步平均,提取行星齿轮箱故障特征。行星齿轮箱故障实测信号分析表明,该方法能有效提取行星齿轮箱故障特征。     

RETRACTED ARTICLE: Development and application of intensified envelope analysis for the condition monitoring system using acoustic emission signal

The application of the high-frequency acoustic-emission (AE) technique in the condition monitoring of rotating machinery has been increasing of late. It has a major drawback, though, the attenuation of the signal, and as such, the AE sensor has to be close to its source. Two signal-processing methods, envelope analysis and wavelet transform, were found to be useful for detecting faults in the rolling element bearing and gearboxes. These methods have a disadvantage, though: their application is focused only on a component of the assembled machine. For example, envelope analysis is a powerful method for detecting faults in the bearing system, but it is not proper for use in the gear system. Thus, these methods could not be used to detect combined faults in the common assembled machines. Therefore, we propose a signal-processing method consisting of envelope analysis and DWT (discrete wavelet transform). In addition, a novel mother function optimized for the AE signal for DWT was extracted through a fatigue crack growth test, and is also proposed herein. Then the proposed method, called intensified envelope analysis (IEA), was used to detect the faults in the rolling element bearing and rotating shaft. According to the results, IEA can be a better signal processing method for the condition monitoring system using AE technique.     

基于自适应时变滤波阶比跟踪的齿轮箱故障诊断

针对多输入多输出齿轮箱传动系统和齿轮箱集群的振动信号中各啮合频率阶次相互干扰,从而导致故障诊断困难的问题,研究提出一种基于自适应时变滤波阶比跟踪的齿轮箱故障诊断方法。该方法利用基于多尺度线调频基稀疏信号分解提取各对传动齿轮的啮合频率,以各啮合频率为中心频率,对应转频的倍频为滤波带宽分别设计自适应时变滤波器对信号进行滤波,逐个提取振动信号中的啮合频率调制分量,再分别对提取的啮合频率调制分量单独进行阶比分析,有效地抑制其他无关联轴上齿轮啮合振动信号和其他非阶比噪声信号对阶比谱的影响,较好地解决阶比信号相互干扰的问题,提高阶比谱的调制识别效果,为多输入多输出齿轮箱系统和齿轮箱集群的故障诊断提供一条有效途径。仿真算例和应用实例说明方法的有效性。     

Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm

Identifying the differences between the spectra or envelope spectra of a faulty signal and a healthy baseline signal is an efficient planetary gearbox local fault detection strategy. However, causes other than local faults can also generate the characteristic frequency of a ring gear fault; this may further affect the detection of a local fault. To address this issue, a new filtering algorithm based on the meshing resonance phenomenon is proposed. In detail, the raw signal is first decomposed into different frequency bands and levels. Then, a new meshing index and an MRgram are constructed to determine which bands belong to the meshing resonance frequency band. Furthermore, an optimal filter band is selected from this MRgram. Finally, the ring gear fault can be detected according to the envelope spectrum of the band-pass filtering result.     

基于时变零相位滤波的变转速滚动轴承故障诊断

针对变转速下齿轮箱中滚动轴承故障调制特征的提取与分离,提出了基于时变零相位滤波的变转速滚动轴承故障诊断方法。该方法先用线调频小波路径追踪(CPP)算法从齿轮箱滚动轴承故障振动信号中估计出齿轮啮合频率,由啮合频率除以齿数得到齿轮箱的转速,同时,采用Hilbert包络解调方法获取轴承故障振动信号的包络信号;然后根据获取的转速信息设计各阶时变零相位滤波器;再采用各时变零相位滤波器对包络信号进行分析,获取各调制信号;最后,利用转速信号对求取的各调制信号进行阶次分析,并根据各阶次谱来诊断滚动轴承故障。算法仿真和应用实例分析表明,该方法可有效提取和分离变速齿轮箱中滚动轴承的各阶故障调制特征。     

基于倒谱预白化和随机共振的轴承故障增强检测

轴承损伤引起的冲击受到离散频率分量和噪声干扰,使轴承故障检测面临困难。结合基于倒谱编辑(Cepstrum editing procedure, CEP)的信号预白化和随机共振(Stochastic resonance, SR)微弱信号检测技术,提出一种轴承故障增强检测的新方法。信号预白化能够提升轴承振动信号的冲击特性,产生包含白噪声和轴承局部故障信号的白化信号。在未知最优共振频带的情况下,对白化后的轴承振动信号进行包络分析,增强故障特征分量的同时引入了较多噪声。通过随机共振的归一化尺度变换,将轴承包络信号作为检测模型的输入,增强轴承故障特征频率分量。提出将轴承故障特征频率处的局部谱峭度和局部信噪比作为对照指标。实测正常和外环植入故障轴承的诊断结果表明,提出的方法优于基于谱峭度优化的包络分析和单纯的信号预白化方法。     

基于角域AR模型滤波的滚动轴承故障诊断

在齿轮噪源存在的变转速滚动轴承故障诊断过程中,因混合信号中转频分量相对较小,使得基于时频表达的阶比跟踪技术受到限制。虽然基于故障特征频率的角域重采样能提取轴承的故障特征,但这种算法不能确定故障位置,而且可能会出现误判。针对这一问题,提出了基于角域自回归(auto regressive,简称AR)模型滤波的处理方法。该方法利用线调频小波路径追踪算法从降采样处理的混合信号中提取齿轮瞬时啮合频率趋势线并估计转速,根据估计转速信息对原混合信号进行等角度重采样,获得了角域信号。利用角域信号中齿轮啮合振动成分具有周期性的特点,使用AR模型对其滤波,并且对滤波后信号进行包络阶比分析,完成故障判断。通过处理仿真信号和实验信号,验证了该方法不仅能有效地去除齿轮噪声,并且可以判断轴承故障位置。