基于噪声辅助分析的总体局部均值分解方法

局部均值分解(Local mean decomposition,LMD)方法是一种新的自适应时频分析方法,但在其实现过程中会发生模态混淆现象,使分析结果失真。通过数值试验得到了LMD对白噪声的滤波器组结构,并在此基础上,针对模态混淆现象提出总体局部均值分解(Ensemble local mean decomposition,ELMD)方法。在该方法中添加不同的白噪声到目标信号,分别对加噪后的信号进行LMD分解,最后将多次分解结果的平均值作为最终的分解结果。对仿真信号和试验转子局部碰摩信号进行分析,结果表明ELMD方法能有效地克服原LMD方法的模态混淆现象。     

Adaptive sparsest narrow-band decomposition method and its applications to rotor fault diagnosis

Enlightened by empirical mode decomposition (EMD) and matching pursuit (MP), adaptive sparsest narrow-band decomposition (ASNBD) method is proposed in this paper. The main idea of the method is to obtain the sparsest representation of a signal by constraining the components to be local narrow-band signals. In ASNBD, an optimized filter must be established at first. The parameters of the filter are determined by solving a nonlinear optimization problem. A regulated differential operator is used as the objective function so that each component is constrained to be a local narrow-band signal. Afterwards, the signal is filtered by the optimized filter to generate a single component. ASNBD is superior to matching pursuit in both the adaptivity and the physical meaning of the components. And problems such as mode mixing and end effect in EMD are alleviated in ASNBD as the computing of extremas is avoided. As it is robust and adaptive to non-stationary signals, artificial chemical reaction optimization algorithm (ACROA) is chosen to solve the optimization problems in ASNBD. Compared with GA, ACROA can reach a global optimum in a shorter time while the classification result is the same. Comparisons are made between ASNBD optimized by ACROA, ASNBD optimized by genetic algorithm and empirical mode decomposition (EMD) by analyzing simulation and experimental signals. The results indicate that ASNBD–ACROA is superior to the other two methods at least in restraining boundary effect, gaining more accurate components in the presence of noise and showing better orthogonality; moreover, it performs better in the analysis of experimental data.     

An improved complementary ensemble empirical mode decomposition with adaptive noise and its application to rolling element bearing fault diagnosis

A novel time–frequency analysis method called complementary complete ensemble empirical mode decomposition (EEMD) with adaptive noise (CCEEMDAN) is proposed to analyze nonstationary vibration signals. CCEEMDAN combines the advantages of improved EEMD with adaptive noise and complementary EEMD, and it improves decomposition performance by reducing reconstruction error and mitigating the effect of mode mixing. However, because white noise mixed in with the raw vibration signal covers the whole frequency bandwidth, each mode inevitably contains some mode noise, which can easily inundate the fault-related information. This paper proposes a time–frequency analysis method based on CCEEMDAN and minimum entropy deconvolution (MED) for fault detection of rolling element bearings. First, a raw signal is decomposed into a series of intrinsic mode functions (IMFs) by using the CCEEMDAN method. Then a sensitive parameter (SP) based on adjusted kurtosis and Pearson’s correlation coefficient is applied to select a sensitive mode that contains the most fault-related information. Finally, the MED is applied to enhance the fault-related impulses in the selected IMF. The fault signals of high-speed train axle-box bearing are applied to verify the effectiveness of the proposed method. Results show that the proposed method can effectively reveal axle-bearing defects’ fault information. The comparisons illustrate the superiority of SP over kurtosis for selecting the sensitive mode from the resulted signal of CCEEMEDAN. Further, we conducted comparisons that highlight the superiority of our proposed method over individual CCEEMDAN and MED methods and over two other popular signal-processing methods, variational mode decomposition and fast kurtogram.     

A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery

In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.     

Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes

Based on the chirplet path pursuit and the sparse signal decomposition method, a new sparse signal decomposition method based on multi-scale chirplet is proposed and applied to the decomposition of vibration signals from gearboxes in fault diagnosis. An over-complete dictionary with multi-scale chirplets as its atoms is constructed using the method. Because of the multi-scale character, this method is superior to the traditional sparse signal decomposition method wherein only a single scale is adopted, and is more applicable to the decomposition of non-stationary signals with multi-components whose frequencies are time-varying. When there are faults in a gearbox, the vibration signals collected are usually AM-FM signals with multiple components whose frequencies vary with the rotational speed of the shaft. The meshing frequency and modulating frequency, which vary with time, can be derived by the proposed method and can be used in gearbox fault diagnosis under time-varying shaft-rotation speed conditions, where the traditional signal processing methods are always blocked. Both simulations and experiments validate the effectiveness of the proposed method.     

An improvement EMD method based on the optimized rational Hermite interpolation approach and its application to gear fault diagnosis

A demodulation technique based on improvement empirical mode decomposition (EMD) is investigated in this paper. Firstly, the problem of the envelope line in EMD is introduced and the drawbacks of two classic interpolation methods, cubic spline interpolation method and cubic Hermite interpolation method are discussed; then a new envelope interpolation method called optimized rational Hermite interpolation method (O-EMD) is proposed, which has a shape controlling parameter compared with the cubic Hermite interpolation algorithm. At the same time, in order to improve the envelope approximation accuracy of local mean, the parameter determining criterion is put forward and an optimization with Genetic Algorithm (GA) is applied to automatic select the suitable shape controlling parameter in each sifting process. The effectiveness of O-EMD method is validated by the numerical simulations and an application to gear fault diagnosis. Results demonstrate that O-EMD method can improve the reliability and accuracy significantly compared with traditional EMD method.     

基于EMD的冲击信号提取方法及其在设备故障诊断中的应用

提出一种基于经验模式分解(Empirical Mode Decomposition,EMD)的冲击信号提取方法,利用该方法首先将含有周期性冲击的信号进行EMD分解,在分解后的高频段IMF中,存在着类似冲击响应信号的成分,这些成分是由原始信号中的周期性冲击引起的,通过包络解调方法,可以得到冲击响应信号出现的频率,该频率对应原信号中冲击信号出现的频率.由于碰摩故障发生时,往往伴随着周期性冲击信号的产生,故该方法可以应用于旋转设备碰摩故障诊断中.仿真信号和试验数据的分析结果表明,这种方法正确有效,可以应用于工程实际.     

An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis

Stochastic resonance (SR) is widely used as an enhanced signal detection method in machinery fault diagnosis. However, the system parameters have significant effects on the output results, which makes it difficult for SR method to achieve satisfactory analysis results. To solve this problem and improve the performance of SR method, this paper proposes an adaptive SR method based on grey wolf optimizer (GWO) algorithm for machinery fault diagnosis. Firstly, the SR system parameters are optimized by the GWO algorithm using a redefined signal-to-noise ratio (SNR) as optimization objective function. Then, the optimal SR output matching the input signal can be adaptively obtained using the optimized parameters. The proposed method is validated on a simulated signal detection and a rolling element bearing test bench, and then applied to the gear fault diagnosis of electric locomotive. Compared with the conventional fixed-parameter SR method, the adaptive SR method based on genetic algorithm (GA-SR) as well as the well-known fast kurtogram method, the proposed method can achieve a greater accuracy. The results indicated that the proposed method has great practical values in engineering.     

A vibration analysis method based on hybrid techniques and its application to rotating machinery

Vibration-based condition monitoring and fault diagnosis technique is a most effective approach to maintain the safe and reliable operation of rotating machinery. Unfortunately, the vibration signal always exhibits non-linear and non-stationary characteristics, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on hybrid techniques is proposed in this paper. Firstly, the raw signals are decomposed into a few product functions (PFs) using local mean decomposition (LMD), and meanwhile instantaneous frequency and instantaneous amplitude also are obtained. Subsequently, Fourier transform is performed on the derived PFs, and then, according to the spectra features, the useful PFs are selected to reconstruct the purified vibration signals. Lastly, several different fault features are fused to illustrate the operating state of the machinery. The experimental results show that the proposed method can accurately extract machine fault features, which proves that the combined application of LMD and other signal processing techniques is a successful scheme for the machine vibration analysis.     

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。     

基于局域均值分解包络谱和SVM的滚动轴承故障诊断方法研究

论述了局域均值分解(Local mean decomposition,LMD)的定义和算法。结合局域均值分解、包络分析和支持向量机(Support vector machine,SVM)的各自特点,提出了一种基于LMD包络谱和SVM的滚动轴承故障诊断方法,该方法先对滚动轴承振动信号进行分解,得到一系列的生产函数分量,然后,再对前面几个生产函数分量进行包络分析,从包络谱中提取特征幅值比作为特征向量输入到SVM分类器中进行识别。实验结果验证了提出的方法的有效性,可以有效地识别滚动轴承的不同故障。     

支持向量机在设备故障诊断方面的应用研究概述

分析了支持向量机（support Vector Machine—SVM）的原理，并就近年来在设备故障诊断方面的应用研究进行了综述，讨论了SVM的优点和不足，展望了其在设备故障诊断的研究前景。     

Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock

The noise reduction effect of singular value decomposition (SVD) relies on the selection of effective singular values. The characteristic of singular values of normal signal and noise being studied, it is pointed out that there is a sudden change in the singular values of normal signal, but not in the ones of noise. The concept of difference spectrum of singular value is put forward, which consists of the forward differences of singular value sequence and can describe the sudden change status of singular values of a complicated signal. The automatic selection of effective singular values can be realized by the peak of the difference spectrum. If the maximum peak of difference spectrum is located in the first coordinates, it means that a strong direct current (DC) component is contained in original signal and the number of effective singular values will be determined by the second maximum peak coordinates, while what the first singular value corresponds to is the DC component, or else the number of effective singular values is determined by the maximum peak coordinates. The relationship between column number of matrix and noise removing quantity of SVD is also studied using difference spectrum and the result shows that this relationship is like a symmetrical parabola. By dint of the difference spectrum, the hidden modulation feature caused by gear vibration in headstock is isolated from a turning force signal and the fault gear is accurately located by this modulation feature.     

小波分析在天然气传输系统压缩机故障检测中的应用

介绍了小波变换的原理及在天然气传输系统故障检测中的应用，利用小波包分析了其主要设备压缩机的故障。结果显示，同傅里叶变换和短时傅里叶变换(STFT)相比，小波变换及小波包变换是更有效的压缩机故障检测方法。     

Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems

In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data. After the sparse signal is reconstructed, the calculated signal is restored with the minimum entropy de-convolution (MED) to get clear fault information. Finally we adopt the sample entropy. Morphological mean square amplitude and the root mean square (RMS) to find the early fault diagnosis of bearing respectively, at the same time, we plot the Boxplot comparison chart to find the best of the three indicators. The experimental results prove that the proposed method can effectively identify the early weak fault diagnosis.     

Identification of motion platform using the signal compression method with pre-processor and its application to siding mode control

In case of a single input single output (SISO) system with a nonlinear term, a signal compression method is useful to identify a system because the equivalent impulse response of linear part from the system can be extracted by the method. However even though the signal compression method is useful to estimate uncertain parameters of the system, the method cannot be directly applied to a unique system with hysteresis characteristics because it cannot estimate all of the two different dynamic properties according to its motion direction. This paper proposes a signal compression method with a pre-processor to identify a unique system with two different dynamics according to its motion direction. The pre-processor plays a role of separating expansion and retraction properties from the system with hysteresis characteristics. For evaluating performance of the proposed approach, a simulation to estimate the assumed unknown parameters for an arbitrary known model is carried out. A motion platform with several single-rod cylinders is a representative unique system with two different dynamics, because each single-rod cylinder has expansion and retraction dynamic properties according to its motion direction. The nominal constant parameters of the motion platform are experimentally identified by using the proposed method. As its application, the identified parameters are applied to a design of a sliding mode controller for the simulator.