Construction of customized redundant multiwavelet via increasing multiplicity for fault detection of rotating machinery

Fault detection from the vibration measurement data of rotating machinery is significant for avoiding serious accidents. However, non-stationary vibration signal with a large amount of noise makes this task challenging. Multiwavelet not only owns the advantage on multi-resolution analysis but also can offer multiple wavelet basis functions. So it has the possibility of detecting various fault features preferably. However, the fixed basis functions which are not related to the given signal may lower the accuracy of fault detection. Moreover, another major intrinsic deficiency of multiwavelet lies in its critically sampled filter-bank, which causes shift-variance and is harmful to extract the feature of periodical impulses. To overcome these deficiencies, a new method called customized redundant multiwavelet (CRM) is constructed via increasing multiplicity (IM). IM is a simple method to design a series of changeable multiwavelet which are available for the subsequent optimization process. By the rule of the envelope spectrum entropy minimum principle, optimal multiwavelet is searched for. Based on the customized multiwavelet filters, the filters of CRM can be calculated by inserting zeros. The proposed method is applied to analyze the simulation, gearbox and rolling element bearing vibration signals. Compared with some other conventional methods, the results demonstrate that the proposed method possesses robust performance in detecting fault features of rotating machinery.     

Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis

Fault diagnosis of rotating machinery is very important and critical to avoid serious accidents. However, the complex and non-stationary vibration signals with a large amount of noise make the fault detection to be challenging, especially at the early stage. Based on the inner product principle, fault detection using wavelet transforms is to match fault features most correlative to basis functions, and its effectiveness is determined by the construction and choice of wavelet basis function. In this paper, a new method based on adaptive multiwavelets via two-scale similarity transforms (TSTs) is proposed. Multiwavelets can offer multiple wavelet basis functions and so have the possibility of matching various fault features preferably. TSTs are simple and straightforward methods to design a series of new biorthogonal multiwavelets with some desirable properties. Using TSTs, a changeable and adaptive multiwavelet library is established so as to provide various ascendant multiple basis functions for inner product operation. By the rule of kurtosis maximization principle, optimal multiwavelets most similar to the fault features of a given signal are searched for. The applications to a rolling bearing of outer-race fault and a flue gas turbine unit of rub-impact fault show that the proposed method is an effective approach to detecting the impulse feature components hidden in vibration signals and performs well for rotating machinery fault diagnosis.     

Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform

In order to enhance the desired features related to some special type of machine fault, a technique based on the dual-tree complex wavelet transform (DTCWT) is proposed in this paper. It is demonstrated that DTCWT enjoys better shift invariance and reduced spectral aliasing than second-generation wavelet transform (SGWT) and empirical mode decomposition by means of numerical simulations. These advantages of the DTCWT arise from the relationship between the two dual-tree wavelet basis functions, instead of the matching of the used single wavelet basis function to the signal being analyzed. Since noise inevitably exists in the measured signals, an enhanced vibration signals denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of vibration signals resulting from a crack gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared to those DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, excavation of comprehensive signatures embedded in the vibration signals is of practical importance to clearly clarify the roots of the fault, especially the combined faults. In the case of multiple features detection, diagnosis results of rolling element bearings with combined faults and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and consistently outperforms SGWT and fast kurtogram, which are widely used recently. Moreover, it must be noted, the proposed method is completely suitable for on-line surveillance and diagnosis due to its good robustness and efficient algorithm.     

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。     

基于LMD和主分量分析的齿轮损伤识别方法

针对齿轮故障振动信号的非平稳特征,提出了基于局部均值分解(Local mean decompos ition,简称LMD)和主分量分析的齿轮损伤识别方法。该方法首先对齿轮振动信号进行局部均 值分解,将其分解成为若干个PF分量(Product function,简称PF),然后选取包含主要损伤信息的PF分量。从PF分量中提取能量和时域统计量等特征参数,组合成初始特征参数向量矩阵, 并进一步对初始特征参数向量矩阵进行主分量分析,得到齿轮振动信号的主特征分量,建立距 离判别函数,从而对齿轮工作状态进行识别。实验数据分析结果表明,本方法能有效地识别齿 轮损伤类型。     

基于自适应随机共振和稀疏编码收缩算法的齿轮故障诊断方法

针对强背景噪声下齿轮故障冲击特征提取问题,提出了一种基于自适应随机共振和稀疏编码收缩算法的齿轮故障诊断方法。该方法选用相关峭度作为随机共振检测周期性冲击分量的测度函数,借助遗传算法实现信号中周期性冲击特征的自适应提取;在此基础上,利用稀疏编码收缩算法对随机共振检测结果做进一步降噪处理,从而凸显冲击特征,提高故障识别精度。试验和工程实例分析结果表明,该方法可实现齿轮故障冲击特征的增强提取,为齿轮故障诊断提供依据。     

Detection of signal transients using independent component analysis and its application in gearbox condition monitoring

This paper addresses feature extraction of the higher-order statistics, which can effectively characterize the transients, using independent component analysis (ICA) for the one-dimensional measured vibration signal, and then proposes a novel automatic technique for detecting the transients in vibration signals with the low signal-to-noise ratio by ICA feature extraction. The basic principle of the ICA-based transient detection method is that the independent components (ICs) coefficients of the transients and the noise can be effectively distinguished by their different sparseness properties. Specifically, the proposed method mainly includes three steps: training the ICA basis features from the signal segments, denoising the sparse ICs coefficients using the shrinkage function deduced by the maximum a posteriori (MAP) estimation, and reconstructing the transient segments by the shrunken coefficients through the ICA basis functions. Experimental results through the simulated signal analysis and the vibration signal analysis show that the ICA-based method is very effective for transient detection outperforming the traditional methods and is valuable for gearbox condition monitoring and fault diagnosis.     

A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis

Gearboxes are widely used in engineering machinery, but tough operation environments often make them subject to failure. And the emergence of periodic impact components is generally associated with gear failure in vibration analysis. However, effective extraction of weak impact features submerged in strong noise has remained a major challenge. Therefore, the paper presents a new adaptive cascaded stochastic resonance (SR) method for impact features extraction in gear fault diagnosis. Through the multi-filtered procession of cascaded SR, the weak impact features can be further enhanced to be more evident in the time domain. By analyzing the characteristics of non-dimensional index for impact signal detection, new measurement indexes are constructed, and can further promote the extraction capability of SR for impact features by combining the data segmentation algorithm via sliding window. Simulation and application have confirmed the effectiveness and superiority of the proposed method in gear fault diagnosis.     

Gear crack level identification based on weighted K nearest neighbor classification algorithm

A crack fault is one of the damage modes most frequently occurring in gears. Identifying different crack levels, especially for early cracks is a challenge in gear fault diagnosis. This paper aims to propose a method to classify the different levels of gear cracks automatically and reliably. In this method, feature parameters in time domain, specially designed for gear damage detection and in frequency domain are extracted to characterize the gear conditions. A two-stage feature selection and weighting technique (TFSWT) via Euclidean distance evaluation technique (EDET) is presented and adopted to select sensitive features and remove fault-unrelated features. A weighted K nearest neighbor (WKNN) classification algorithm is utilized to identify the gear crack levels. The gear crack experiments were conducted and the vibration signals were captured from the gears under different loads and motor speeds. The proposed method is applied to identifying the gear crack levels and the applied results demonstrate its effectiveness.     

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。     

利用HHT和重采样技术分析瞬态机械信号

介绍了HHT理论及数据重采样技术的实现方法。分析了利用HHT理论和瞬态信号进行机械故障诊断时常用故障特征提取方法的不足。针对瞬态信号中所包含的故障特征是随时间的变化而变化的特点,研究了将数据重采样技术与HHT理论相结合的瞬态信号分析方法。以齿轮箱齿面磨损故障为例进行了算例分析,通过对照分析原始信号与重抽样信号的分析结果,说明了该方法的有效性。     

Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis

The characteristic signal of a rolling bearing with a defect acts as a series of periodic impulses. These features are usually immersed in heavy noise and then difficult to extract. It is feasible to make the features distinct through wavelet denoising. Scalar wavelet thresholding has been used to extract features. However, scalar wavelet might not extract the feature available due to its limitation in some important properties, and conventional term-by-term thresholding does not consider the effect of neighboring coefficients. Since multiwavelets have been formulated recently and they might offer good properties in signal processing, a novel denoising method — multiwavelet denoising with improved neighboring coefficients (neighboring coefficients dependent on level, DLNeighCoeff for short) — is proposed in this article. The method proposed is applied to a simulated signal and fault diagnosis of locomotive rolling bearings, obtaining performance superior to conventional methods.     

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.     

The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform

The vibration signal of a gear system is selected as the original information of fault diagnosis and the gear system vibration equipment is established. The vibration acceleration signals of the normal gear, gear with tooth root crack fault, gear with pitch crack fault, gear with tooth wear fault and gear with multi-fault (tooth root crack & tooth wear fault) is collected in four kinds of speed conditions such as 300 rpm, 900 rpm, 1200 rpm and 1500 rpm. Using the method of wavelet threshold de-noising to denoise the original signal and decomposing the denoising signal utilizing the wavelet packet transform, then 16 frequency bands of decomposed signal are got. After restructuring the decomposing signal and obtaining the signal energy in each frequency band, the signal energy of the 16 bands is as the shortlisted fault characteristic data. Based on this, using the methods of principal component analysis (short for PCA) and kernel principal component analysis (short for KPCA) to extract the feature from the fault features of shortlisted 16-dimensional data feature, then the effect of reducing dimension analysis are compared. The fault classifications are displayed through the information that got from the first and the second principal component and kernel principal component, and these demonstrate they have a different and good effect of classification. Meanwhile, the article discusses the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters. These provide a new method for a gear system fault feature extraction and classification.     

基于DataSocket和小波消噪的齿轮故障远程监测与诊断

介绍利用LabVIEW平台检测齿轮故障信号 ,叙述DataSocket协议和使用DataSocket技术进行远程监控的方法 ,给出在LabVIEW的环境内 ,使用MATLAB脚本节点对齿轮振动信号进行小波消噪和分解 ,提取齿轮故障特征信息 ,实现齿轮故障的远程诊断的方案。     

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。     

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。     

基于高阶谱的齿轮故障特征提取方法研究

基于高阶谱能够抑制高斯信号,并且可以在较强的背景噪声中提取故障信息的特点,在分析高阶谱的理论基础上,针对齿轮振动信号的非线性、频谱成分多样性等特点,研究了基于高阶谱分析的机械故障特征提取方法,提出了基于双谱估计的齿轮故障诊断方法.试验结果表明,该方法能够有效地将正常及不同裂纹程度的齿轮区分开来.     

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.