Gearbox fault detection using Hilbert and wavelet packet transform

Demodulation is an important issue in gearbox fault detection. Non-stationary modulating signals increase difficulties of demodulation. Though wavelet packet transform has better time–frequency localisation, because of the existence of meshing frequencies, their harmonics, and coupling frequencies generated by modulation, fault detection results using wavelet packet transform alone are usually unsatisfactory, especially for a multi-stage gearbox which contains close or identical frequency components. This paper proposes a new fault detection method that combines Hilbert transform and wavelet packet transform. Both simulated signals and real vibration signals collected from a gearbox dynamics simulator are used to verify the proposed method. Analysed results show that the proposed method is effective to extract modulating signal and help to detect the early gear fault.     

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.     

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。     

Application of a new parametric model-based filter to knock intensity measurement

In this paper, a new parametric model-based filter is proposed for impact-induced fault intensity measurement. The filter designing steps are based on advanced parametric modeling and decomposition of the non-stationary random vibration signals, extracted from a mechanical system. One of the important innovations of this work throughout filter designing is proposing a method for identifying the most proper latent component of the signal in the undamaged state using instant modules of the signal. By considering this fact that knock is one of the important defects in the spark ignition engines, the proposed filter is applied for knock intensity measurement as a case study. For evaluation purpose, the proposed method is compared with other previous methods in literatures.     

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.     

基于自混合干涉的齿轮箱故障诊断技术

将激光自混合干涉（SMI）技术用于齿轮箱的故障检测,设计出一种新的齿轮箱故障检测传感器。采用QL65D5SA型半导体激光自混合传感器、冯哈勃2342l012CR空心杯减速电机自带的行星齿轮箱,搭建了行星齿轮箱故障SMI检测系统,并对行星轮Z1做断齿故障实验。通过对时域波形的分析,可以找到额定转频下的12个冲击点;通过对齿轮箱故障信号傅里叶频谱的分析,发现故障齿轮的啮合频率周围出现与故障齿轮特征频率和行星架转频呈整数倍关系的边带,且啮合频率处的波形幅值明显增大,这些都与齿轮副的理论振动模型相符合。     

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。     

Adaptive state detection of gearboxes under varying load conditions based on parametric modelling

Conventional vibration monitoring techniques are unable to provide accurate state analysis of a gearbox under varying load condition. This paper proposes a novel technique for state detection of gearbox, which fits a time-varying autoregressive model to the gear motion residual signals applying a noise-adaptive Kalman filter, in the healthy state of the target gear. The optimum autoregressive model order, which provides a compromised model fitting for the healthy gear motion residual signals collected under various load conditions, is determined with the aid of a specific model order selection method proposed in this study. Consequently, a robust statistical measure, which takes the percentage of outliers exceeding the three standard deviation limits is applied to evaluate the state of the target gear, where the standard deviation of autoregressive model residuals takes its maximum in all tested gear motion residual signals for model order selection. The proposed technique is validated using full lifetime vibration data of gearboxes operating from new to failure under four distinct load conditions. The investigated load conditions include: (1) constant load, (2) one jump from 100 to 200% nominal torque level, (3) one jump from 100 to 300% nominal torque level, and (4) constant changed to sinusoidal. In each application, the specific model order selection and comparison of the proposed gear state indicator with three counterparts proposed in recent studies are addressed in detail. The Kolmogorov–Smirnov test is also performed as a complementary statistical analysis. The results show that the proposed technique possesses a highly effective and robust property in the state detection of gearbox, which is independent of varying load condition as well as remarkable stability, early alarm for incipient fault and significant presence of fault effects. The proposed gear state indicator can be directly employed by an on-line maintenance program as a reliable quantitative covariate to schedule optimal maintenance decision for rotating machinery.     

复小波分解联合SVD提取振动信号非平稳特征

针对齿轮箱故障信号的多分量多频调制特点,提出了一种基于奇异值分解的最优小波解调技术。首先,采用小波变换的最小Shannon熵作为时间尺度分辨率的度量指标,将其应用到Morlet分析小波的参数优化选择中;其次,对常规小波参数选择方法进行了改进,利用奇异值分解技术对最优小波变化尺度进行了迭代搜索。该方法可以很好地降低噪声信号,有效提取信号中的周期成分,具有较好的瞬态信息提取能力。试验结果也表明了该方法在齿轮箱故障特征提取中的重要性以及降噪方法的有效性。     

Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Fault diagnosis of gearboxes, especially the gears and bearings, is of great importance to the long-term safe operation. An unexpected damage on the gearbox may break the whole transmission line down. It is therefore crucial for engineers and researchers to monitor the health condition of the gearbox in a timely manner to eliminate the impending faults. However, useful fault detection information is often submerged in heavy background noise. Thereby, a new fault detection method for gearboxes using the blind source separation (BSS) and nonlinear feature extraction techniques is presented in this paper. The nonstationary vibration signals were analyzed to reveal the operation state of the gearbox. The kernel independent component analysis (KICA) algorithm was used hereby as the BSS approach for the mixed observation signals of the gearbox vibration to discover the characteristic vibration source associated with the gearbox faults. Then the wavelet packet transform (WPT) and empirical mode decomposition (EMD) nonlinear analysis methods were employed to deal with the nonstationary vibrations to extract the original fault feature vector. Moreover, the locally linear embedding (LLE) algorithm was performed as the nonlinear feature reduction technique to attain distinct features from the feature vector. Lastly, the fuzzy k-nearest neighbor (FKNN) was applied to the fault pattern identification of the gearbox. Two case studies were carried out to evaluate the effectiveness of the proposed diagnostic approach. One is for the gear fault diagnosis, and the other is to diagnose the rolling bearing faults of the gearbox. The nonstationary vibration data was acquired from the gear and rolling bearing fault test-beds, respectively. The experimental test results show that sensitive fault features can be extracted after the KICA processing, and the proposed diagnostic system is effective for the multi-fault diagnosis of the gears and rolling bearings. In addition, the proposed method can achieve higher performance than that without KICA processing with respect to the classification rate.     

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。     

A multi-order FRFT self-adaptive filter based on segmental frequency fitting and early fault diagnosis in gears

To effectively diagnose gear failure at an early stage, a multi-order Fractional Fourier transform (FRFT) self-adaptive filter based on segmental frequency fitting (MFSFF) is proposed to separate the feature components with curved frequency from the gearbox’s transient conditions. First, a linear multi-scale segmentation method (LMSS) is developed to divide the signal with curved frequency into segments with nearly linear frequency; then, a method for determining the FRFT filter parameters by fitting the frequency curve (DFFPFF) is developed to calculate the FRFT filter parameters for each signal segment, and the signal in each segment is filtered by an FRFT filter using these parameters to determine the MFSFF. The vibration of the gearbox’s acceleration and deceleration process is analyzed using an MFSFF and the filtered signal is demodulated. The experimental results show that LMSS is able to divide any signal with curved frequency into minimal segments with nearly linear frequency; DFFPFF is exact, fast, not influenced by the vibration source or the number of components, and able to determine the FRFT filter parameters for each signal segment accurately; the feature component of the gearbox’s transient condition is accurately extracted by an MFSFF, and the other components and noise are removed simultaneously. Early gear failure is diagnosed exactly by demodulation of the extracted feature component, which is difficult to identify using the traditional method.     

A method for the compound fault diagnosis of gearboxes based on morphological component analysis

An improved morphological component analysis (MCA) method is proposed for the compound fault diagnosis of gearboxes. When gear fault and bearing fault occur simultaneously, the compound fault signal of the gearbox contains meshing components (related to the gear fault) and periodic impulse components (related to the bearing fault). The corresponding fault characteristics can be separated by MCA according to the morphological differences of the components. In the proposed method, the optimal dictionary, which can represent the characteristics of bearing faults, is first selected based on the principle of minimum information entropy. Then, the compound fault signal is decomposed into the meshing component and the periodic impulse component using MCA. Finally, the separated components are subjected to the Hilbert envelope spectrum analysis. The faults of the gear and the bearing can be diagnosed according to the envelope spectra of the separated fault signal components. Simulation and experimental studies validate the effectiveness of the proposed method for the compound fault diagnosis of gearboxes.     

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.     

基于自组织映射的齿轮箱状态监测可视化研究

提出了一种自组织映射网络训练结果的可视化方法——距离映射法,该方法通过计算出竞争层神经元权矢量与输入模式的相似度,并综合考虑神经元的网格分布,把输入矢量降维映射到二维平面。结合该方法研究了自组织映射网络在齿轮箱故障识别和状态监测中的应用。与U－矩阵法相比,该方法能更加清楚地将齿轮正常、裂纹和断齿状态的特征数据映射到二维平面的不同区域,将齿轮箱状态聚类分开,特征数据在平面上的映像点轨迹变化趋势直观反映了齿轮箱工作状态的变化,便于及时监测识别出齿轮的早期故障及其变化趋势。     

Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review

Reliable recognition of fault type and assessment of fault severity is essential for decision making in condition-based maintenance of gear transmission systems. In engineering practice, the gear systems are often subject to hybrid faults on the same component or different components. The concurrence of multiple faults makes the fault detection, in particular, the examination of both the fault types and severities, more challenging. Recently, this research area has been recognized as an important direction. A logic solution is to decouple the hybrid faults. This paper reviews various aspects of recent research in decoupling diagnosis of hybrid faults in gear transmission systems, and discusses the techniques used for gearbox hybrid faults decoupling. The general fault detection technologies for gearboxes are also briefly summarized. A potential methodology based on the bounded component analysis (BCA) for hybrid faults decoupling is discussed. Possible future research trends of gearbox hybrid faults decoupling diagnosis are suggested.     

Application of Instantaneous Rotational Speed to Detect Gearbox Faults Based on Double Encoders

Considerable studies have been carried out on fault diagnosis of gears, with most of them concentrated on conventional vibration analysis. However, besides the complexity of gear dynamics, the diagnosis results in terms of vibration signal are easily misjudged owing to the interference of sensor position or other components. In this paper, an alternative gearbox fault detection method based on the instantaneous rotational speed is proposed because of its advantages over vibration analysis. Depending on the timer/counter-based method for the pulse signal of the optical encoder, the varying rotational speed can be obtained e ectively. Owing to the coupling and meshing of gears in transmission, the excitations are the same for the instantaneous rotational speed of the input and output shafts. Thus, the di erential signal of instantaneous rotational speeds can be adopted to eliminate the e ect of the interference excitations and extract the associated feature of the localized fault e ectively. With the experiments on multistage gearbox test system, the di erential signal of instantaneous speeds is compared with other signals. It is proved that localized faults in the gearbox generate small angular speed fluctuations, which are measurable with an optical encoder. Using the di erential signal of instantaneous speeds, the fault characteristics are extracted in the spectrum where the deterministic frequency component and its harmonics corresponding to crack fault characteristics are displayed clearly.     

一种基于EMD与多特征支持向量机(SVM)故障诊断方法

针对齿轮箱故障振动信号的不平稳非线性冲击行为,本文提出了一种基于经验模态分解的特征值提取及多特征支持向量机的智能诊断方法。在电机频率分别取30 Hz、35 Hz、40 Hz;载荷分别取0 N∙M、15 N∙M、30 N∙M;采样频率为1500 Hz条件下,进行齿轮正常状态、齿面磨损和齿轮裂痕故障模拟实验。试验结果表明:该创新方法在有限样本数据分析中可以准确、有效地对齿轮箱的工作状态和故障类型进行分类,且支持向量机在故障诊断中使用方便,可以提高诊断的精确性,在齿轮箱故障诊断或类似振动信号的检测应用中具有很强的实用性。     

核函数主元分析及其在齿轮故障诊断中的应用

提出了基于核函数主元分析的齿轮故障诊断方法。该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析,得到原始特征的非线性主元,以所选的非线性主元作为特征子空间对齿轮工作状态进行分类识别。用齿轮在正常状态、裂纹状态和断齿状态下的试验数据对该方法进行了检验,比较了主元分析与核函数主元分析的分类效果。结果表明,核函数主元分析能有效的检测裂纹故障的出现,正确区分不同的故障模式,更适于提取故障信号的非线性特征。     

齿轮点蚀的多通道数据融合识别方法

针对齿轮箱振动信号中混杂其他零部件振动频率的问题,提出一种基于小波包分解独立分量分析（wavelet package independent component analysis,简称WPICA）和多维经验模式分解（multivariate empirical mode decomposition,简称MEMD）的齿轮箱齿面点蚀故障信号的多通道数据融合识别方法。首先,利用一种窄带独立分量分析(sub-band decomposition independent component analysis,简称SDICA)方法—WPICA,从水泵机组多通道信号中提取齿轮箱振源,确定齿轮箱振动包含的特征频率成分;其次,借助MEMD分解多通道机组振动信号,将所获得的多维固有模式函数(intrinsic mode function,简称IMF)进行矩阵互信息运算,完成多通道数据的融合;最后,通过定义IMF故障敏感因子,确定故障敏感IMF的阶数并获得了齿轮点蚀故障的特征频率。数据分析结果证明了本研究方法的有效性。