基于NLSTFT与TSA相结合的风电齿轮箱故障诊断

针对变工况风电齿轮箱振动信号存在频谱频率模糊问题,以及传统时域同步平均方法需要键相信号及转速稳定要求.提出了一种不需要键相信号可跟踪变转速振动信号瞬时频率的时域同步平均方法.该方法通过非线性短时傅里叶变换(Non-linear short-time fourier transform,NLSTFT)获取变转速齿轮箱振动信号瞬时频率曲线,积分得到瞬时相位曲线;根据瞬时相位对原始信号进行角域重采样,获得阶次信号;最后对阶次信号进行TSA处理进行齿轮故障诊断.以某机组的齿轮箱实测数据,有效地验证了所提方法在风电齿轮箱故障诊断中的有效性及工程实用性.     

基于阶次跟踪的变速齿轮啮合频率振动评估方法

针对变速齿轮在某一转速范围内运行时产生共振导致强烈振动的问题,有效地对变速齿轮啮合频率进行评估是减少和规避此类故障的关键。但由于变速齿轮振动信号在时域呈现非平稳性,传统的齿轮信号分析方法无法有效地提取出特征频率,并且齿轮箱振动源较多且存在背景噪声,导致阶次谱成分复杂。有鉴于此,将阶次跟踪和经验模态分解相结合,提出一种基于阶次跟踪的变速齿轮啮合频率振动评估方法。首先采用阶次跟踪方法对某转速下齿轮的时域信号进行等角度重采样;然后对阶次域信号进行经验模态分解,提取出包含齿轮啮合信息的本征模函数(Intrinsic Mode Function,IMF)分量;再利用阶次谱分析提取出齿轮啮合阶次所对应的幅值;最后通过比较不同转速下的齿轮啮合阶次的幅值大小,对齿轮的振动变化进行评估。在实际现场齿轮信号的应用结果表明:该方法得到的阶次谱中啮合阶次明显,并且能够有效地实现变速齿轮的共振转速识别。     

瞬时频率估计的齿轮箱升降速信号阶次跟踪

提出了基于瞬时频率估计的齿轮箱升降速信号阶次跟踪的新方法。首先对振动信号进行经验模态分解得到信号的固有模态函数，再求各个固有模态函数的Hilbert变换，得到信号的瞬时频率，从而直接从振动信号得到参考轴的转速信号，然后根据参考轴的转速信号对时域振动信号进行等角度重采样，最后对重采样信号进行阶次分析。通过仿真信号和对齿轮磨损故障实验信号的分析，表明该方法能有效地诊断齿轮的故障。     

基于VMD阶比跟踪的变转速齿轮箱齿轮故障特征提取

针对变转速工况下齿轮箱齿轮阶比信号互相干扰故障特征难以提取的问题,提出了基于VMD(Variational Mode Decomposition)和阶比跟踪技术结合的齿轮箱齿轮故障特征提取方法。该方法通过计算阶比跟踪技术对振动信号进行角域重采样;获得重采样信号后,利用VMD按照中心阶比不同,自适应地将重采样信号分解,再利用峭度准则从IMF(Intrinsic Mode Function)分量中选取出故障信号;最后对故障信号进行快速谱峭图处理和滤波平方包络解调。通过变转速下齿轮箱的齿轮故障试验和对比分析,表明该方法能有效提取出变转速下齿轮箱的齿轮故障特征,且降噪效果明显,特征突出,适用于变转速齿轮箱的齿轮故障特征提取。     

基于局部均值分解的阶次跟踪分析及其在齿轮故障诊断中的应用

针对齿轮升降速过程中故障振动信号为多分量的调制信号以及故障特征频率随转速变化的特点,将局部均值分解（LMD）与阶次跟踪分析相结合,提出了一种新的齿轮故障诊断方法。首先采用阶次重采样将齿轮的时域振动信号转换为角域平稳信号,然后对角域信号进行LMD分解,得到若干个乘积函数（PF）分量,最后对各个PF分量的瞬时幅值进行频谱分析来提取齿轮的故障特征。通过对齿轮齿根裂纹故障试验振动信号的分析可知,该方法能有效地提取齿轮故障特征。     

齿轮箱起动过程故障诊断

针对齿轮箱升降速过程中振动信号非平稳的特点,将阶次跟踪、角域平均和Teager能量算子分析技术相结合,提出了基于阶次跟踪和Teager能量算子分析的齿轮箱故障诊断方法.首先对齿轮箱升降速瞬态信号进行时域同步采样,再对时域信号进行等角度重采样,转化为角域平稳信号,然后对角域信号进行角域平均和带通滤波,以消除干扰噪声的影响,最后由Teager能量算子计算振动信号的瞬时频率和瞬时幅值,根据瞬时频率和瞬时幅值图,就可提取齿轮的故障特征.通过对齿轮齿根裂纹故障试验信号的分析,表明该方法能有效地诊断齿轮的裂纹故障.     

基于c．RIO的阶次跟踪分析在风力发电中的应用

针对风力发电机组齿轮箱变速过程中振动信号非平稳的特点,将阶次跟踪和信号包络提取技术相结合,提出了一种针对齿轮点蚀故障的诊断方法。首先利用Compact RIO对齿轮箱的振动信号进行了时域数据采集,然后对时域信号进行了包络提取,进而对时域包络信号进行等角域重采样得到等角域包络信号,最后对等角域包络信号进行了阶次跟踪分析;通过对比正常齿轮和点蚀故障齿轮的包络阶次谱,进而找到了点蚀故障齿轮的故障频率特征。模拟仿真结果表明,阶次跟踪分析可以解决传统傅里叶变换在处理非平稳信号时的“频谱模糊”现象。通过齿轮点蚀故障试验的分析,结果表明包络阶次谱能够用于有效地分析出点蚀故障齿轮的特征频率,阶次跟踪分析在风力发电机组齿轮箱的故障诊断中具有广阔的应用前景。     

POVMD与包络阶次谱的变工况滚动轴承故障诊断

针对变转速滚动轴承故障特征提取较难的问题,提出一种基于参数优化变分模态分解(parameter optimized variational mode decomposition,简称POVMD)与包络阶次谱的变工况滚动轴承故障诊断方法。首先,采用POVMD对变转速滚动轴承振动信号进行分解,得到若干个本征模态函数之和;其次,对各个分量的时域信号进行角域重采样,将时变信号转化为平稳信号处理,再利用Hilbert变换估计重采样后的平稳信号的包络;最后,对得到的包络信号进行阶比分析,从谱图中读取故障特征信息。将POVMD方法与经验模态分解进行了对比,仿真信号分析结果表明了POVMD方法的优越性。将提出的变转速滚动轴承故障诊断方法应用于试验数据分析,分析结果表明,所提出的方法能够实现变转速滚动轴承的故障诊断,而且诊断效果优于现有方法。     

非稳态信号计算阶次分析中的重采样率研究

对于转频不断变化的旋转机械振动信号,运用阶次跟踪分析方法能够避免常规快速傅里叶分析中出现的“频率模糊”现象。在试验设计阶段,时域采样率凭经验设定,角域重采样信号存在阶次混迭现象。分析了角域重采样和时域采样的关系,得出在实际操作中由所需角域分析带宽和预期转速确定时域采样率,再由采样率和实际转速确定实际角域重采样阶次和分析带宽,推导出了时域采样频率和采样阶次必须满足的条件,进行了仿真算例分析。最后,以某型航空发动机起动时的振动信号为例,进行状态检测分析,验证了该方法的正确性和有效性。     

运用阶次跟踪和奇异谱降噪诊断齿轮早期故障

针对齿轮箱升降速过程中振动信号非平稳的特点,将阶次跟踪分析与奇异谱降噪技术相结合,提出了一种针对齿轮早期故障的诊断方法。首先对齿轮箱加速时测得的瞬态信号进行时域采样,再对时域信号进行等角域重采样,转化为角域伪稳态信号;然后对角域信号进行奇异谱降噪处理,以减小背景噪声的影响;最后对降噪后的信号进行阶次谱分析。通过对齿轮箱早期故障信号的分析表明,该方法能准确地识别出齿轮的故障特征。     

Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed

Filtering techniques are used to improve the signal to noise ratio (SNR) for better feature extraction. The time synchronous averaging (TSA) is one of such method that is based on averaging periodic sections. However, it fails to give significant results for an asynchronous or fluctuating speed condition. Moreover, most of the real life applications of gear are in asynchronous conditions. The aim of this paper is to develop a methodology which is robust for fault detection of gears under fluctuating load and speed conditions. A multiple-pulse individually rescaled-time synchronous averaging (MIR-TSA) technique in conjunction with conventional time synchronous averaging has been proposed. A 2-D finite element methodology based on principal or linear elastic fracture mechanics is adopted for predicting the crack propagation path at the root of gear tooth. The crack has been introduced using wire electrode discharge machining (WEDM). The vibration signals were recorded using drivetrain dynamic simulator (DDS) setup for various combination of load and crack length both for constant as well as fluctuating speed. Various time domain features such as root mean square, crest factor and kurtosis have been calculated using classical TSA and proposed MIR-TSA. A comparison of different extracted features between the proposed method and classic TSA has also been outlined. It has been observed that the proposed method enhances the fault detection under fluctuating speed conditions.     

基于高阶累积量的齿轮裂纹故障检测与识别

介绍了高阶累积量理论,分析了它提取复杂耦合信号和非线性特性的理论基础。在此基础上将高阶累积量方法应用于机械齿轮故障特征分析诊断之中,对无故障齿轮和故障齿轮受力激振后的振动耦合信号进行了分析和特征提取,从而成功地实现了对不同裂纹类故障的识别与诊断,通过与传统的功率谱等方法的比较,说明了高阶累积量方法在齿轮故障诊断中的可靠性和有效性。     

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。     

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.This study reveals the most relevant features to be used for damage detection and condition monitoring of the gear system. It is also shown that the proposed procedure using a combination of cyclostationarity and autoregressive modeling enhance the ability to detect and diagnose mechanical wear in multi-staged planetary gears.     

Novel adaptation of the demodulation technology for gear damage detection to variable amplitudes of mesh harmonics

In this paper, a novel adaptive demodulation technique including a new diagnostic feature is proposed for gear diagnosis in conditions of variable amplitudes of the mesh harmonics. This vibration technique employs the time synchronous average (TSA) of vibration signals. The new adaptive diagnostic feature is defined as the ratio of the sum of the sideband components of the envelope spectrum of a mesh harmonic to the measured power of the mesh harmonic. The proposed adaptation of the technique is justified theoretically and experimentally by the high level of the positive covariance between amplitudes of the mesh harmonics and the sidebands in conditions of variable amplitudes of the mesh harmonics. It is shown that the adaptive demodulation technique preserves effectiveness of local fault detection of gears operating in conditions of variable mesh amplitudes.     

Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.     

基于复小波变换相位谱的齿轮故障诊断

提出了一种基于复小波变换诊断齿轮故障的新方法。利用Mexican-hat调制复小波基函数对齿轮振动信号进行连续小波变换，再作相位的频谱分析，可以突出边频带结构，识别不同故障模式。试验数据的分析结果表明，该方法适用于齿轮故障诊断，与传统的自功率谱方法以及基于实值小波的小波变换方法相比，这种方法效果更好。     

An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor

In this paper we extend a sensorless algorithm proposed by Bonnardot et al. for angular resampling of the acceleration signal of a gearbox submitted to limited speed fluctuation. The previous algorithm estimates the shaft angular position by narrow-band demodulation of one harmonic of the mesh frequency. The harmonic was chosen by trial and error. This paper proposes a solution to select automatically the mesh harmonic used for the shaft angular position estimation. To do so it evaluates the local signal-to-noise ratio associated to the mesh harmonic and deduces the associated low-pass filtering effect on the time synchronous average (TSA) of the signal. Results are compared with the TSA obtained when using a tachometer on an industrial gearbox used for wastewater treatment. The proposed methodology requires only the knowledge of an approximate value of the running speed and the number of teeth of the gears. It forms an automated scheme which can prove useful for real-time diagnostic applications based on TSA where speed measurement is not possible or not advisable due to difficult environmental conditions.     

基于自适应最大相关峭度解卷积和频率切片小波变换的齿轮故障特征提取

针对最大相关峭度解卷积(MCKD)降噪效果受滤波器阶数影响的问题,提出了自适应MCKD方法。针对频率切片小波变换(FSWT)在强背景噪声中提取冲击故障特征的不足,提出了自适应MCKD和FSWT相结合的齿轮故障特征提取方法。首先用自适应MCKD对噪声齿轮信号进行降噪处理,然后对降噪后的信号进行频率切片小波变换和故障特征提取。齿轮故障诊断实例的分析结果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.