基于多稳态随机共振的轴承微弱故障信号检测

针对双稳态随机共振模型无法有效处理调制信号的缺点,提出了一种以包络信号为输入信号的自适应多稳态级联随机共振(adaptive multi-stable cascaded stochastic resonance,简称AMCSR)信号强化方法。首先,对振动信号进行包络解调,依据包络信号分布特点,选用与信号分布相匹配的多稳态随机共振模型;然后,以故障特征频率的频谱幅值为指标,采用蚁群算法自适应地优化随机共振模型参数;最后,以噪声为强化源和驱动信号,通过级联随机共振方法对包络信号中的故障特征频率进行逐级强化,获得故障特征成分的强化信号。对实测轴承振动信号的验证结果表明,该方法能够增强故障特征频率成分,有效地提取被其他频率成分淹没的微弱故障信号。     

基于幂函数型双稳随机共振的故障信号检测方法

在实际的故障诊断中,有用信号经常淹没在噪声中,特征信息提取非常困难。为了提取强噪声背景中的微弱信号,将幂函数型单势阱模型与Gaussian Potential模型相结合提出一种新型的双稳随机共振系统,称为幂函数型双稳随机共振系统。首先,以平均信噪比增益为衡量指标,提出一种寻找最优系统参数组合的算法,使微弱信号、噪声及系统产生最佳的共振效果;然后,基于幂函数型双稳随机共振系统对Levy噪声背景下的仿真信号进行检测;最后提出一种基于小波变换和幂函数型双稳随机共振的微弱信号检测方法并应用于轴承故障信号检测中。仿真实验表明,幂函数型双稳随机共振模型在故障信号检测中是有效和可靠的。     

基于PPCA-EWT的滚动轴承轻微故障诊断

针对经验小波变换(empirical wavelet transform,简称EWT)在强背景噪声下对轴承的轻微故障特征提取不足的问题,提出了概率主成分分析(probabilistic principal component analysis,简称PPCA)结合EWT的滚动轴承轻微故障诊断方法。首先,对信号做PPCA预处理,提取信号主要故障特征成分,去除强背景噪声干扰;然后,采用EWT方法分解轴承故障信号,按相关系数-峭度准则选出故障特征较为明显的分量,并将所选分量重构故障信号;最后,对信号采取包络分析,提取出轴承故障特征。仿真和实验结果表明,该方法能够有效地诊断出轴承故障且效果优于对信号进行EWT包络分析。     

Continuous wavelet grey moment approach for vibration analysis of rotating machinery

The statistical characteristics of the continuous wavelet transform scalogram of vibration signal are explored, and two features, wavelet grey moment (WGM) and first-order wavelet grey moment vector (WGMV), are proposed for condition monitoring of rotating machinery. Faulty signals from a rotor test rig are studied using the first-order WGM. The analysis indicates that first-order WGM can reveal the time–frequency features of vibration signals well and that it could be used to diagnose faults quantitatively. The effectiveness of the first-order WGMV is also demonstrated by experimental data. Results show that the first-order WGMV is suitable to reflect the local information of scalogram, and would be an effective method of vibration signal analysis for fault diagnostics of rotating machinery. The result by FFT analysis is also given. Compared with the conventional FFT method, the two features presented in this paper are more suitable to extract characters of faults quantitatively.     

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.     

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.     

Vibration component separation by iteratively using stochastic resonance with different frequency-scale ratios

It is well-known that stochastic resonance (SR) is mainly used for signal denoising and weak signal detection. In this paper, we firstly find the frequency range selection characteristic (filtering characteristic) of re-scaling frequency SR (RFSR) caused by the driving frequency limitation of bistable SR. It then follows that a novel approach to separate vibration components with different frequencies by iteratively using SR is explored. The frequencies of most vibration signals exceed the driving frequency limitation, thus by use of different frequency-scale ratios, the vibration signals with different frequency range can be extracted by RFSR. Firstly, a small frequency-scale ratio is used to obtain the vibration signal with a narrow frequency range, i.e. low frequency vibration. As the output of SR may have a phase lag, a simple phase-shift correction method is proposed to improve the accuracy of signal component separation. The phase-shift corrected signal of RFSR output is separated from the original vibration signal and the residue is treated as the new vibration signal. Then, increasing the frequency-scale ratio according to a searching algorithm, the vibration signal with higher frequency can be obtained by RFSR. Through this iterative process, several harmonic vibration components can be separated from the original noisy vibration signal. The proposed method, empirical mode decomposition (EMD) and Hilbert vibration decomposition (HVD) are respectively applied to analyzing a simulated vibration signal and extracting the fault feature of a rotor system. The contrastive results show that this proposed method has good frequency resolution and can successfully separate monocomponent harmonic signals from a strongly noisy multicomponent harmonic vibration signal while EMD and HVD cannot.     

变风速条件下风力机轴承故障特征提取

针对风速变化条件下风力发电机轴承故障特征的检测问题,提出了一种基于灰狼优化( GWO )和双稳态杜芬振荡器的随机共振( SR )的故障特征提取方法.首先,根据风速估计故障特征信号的频率,通过合适的采样频率采集风力发电机的振动信号并对采集的信号做归一化处理.随后,根据风速尺度引入变换系数对频率 时间尺度进行变换.此外,利用灰狼算法方法将杜芬振子的阻尼比和系统参数调整到最优值.最后,通过杜芬系统和尺度恢复获得可识别信号.结果表明,所提出的方法能提取原始信号中的故障特征信号.     

Anti-aliasing lifting scheme for mechanical vibration fault feature extraction

A troublesome problem in application of wavelet transform for mechanical vibration fault feature extraction is frequency aliasing. In this paper, an anti-aliasing lifting scheme is proposed to solve this problem. With this method, the input signal is firstly transformed by a redundant lifting scheme to avoid the aliasing caused by split and merge operations. Then the resultant coefficients and their single subband reconstructed signals are further processed to remove the aliasing caused by the unideal frequency property of lifting filters based on the fast Fourier transform (FFT) technique. Because the aliasing in each subband signal is eliminated, the ratio of signal to noise (SNR) is improved. The anti-aliasing lifting scheme is applied to analyze a practical vibration signal measured from a faulty ball bearing and testing results confirm that the proposed method is effective for extracting weak fault feature from a complex background. The proposed method is also applied to the fault diagnosis of valve trains in different working conditions on a gasoline engine. The experimental results show that using the features extracted from the anti-aliasing lifting scheme for classification can obtain a higher accuracy than using those extracted from the lifting scheme and the redundant lifting scheme.     

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.     

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.     

基于数学形态变换的转子故障特征提取方法

基于非线性数学形态变换提出旋转机械故障特征提取的新方法.由数学形态变换构成的形态滤波器可以有效地提取出信号的边缘轮廓以及形状特征,通过选取不同长度的形态结构元素,采用组合形态滤波器将旋转机械故障信号分解到不同频带上,故障信号被分解成基频成分、故障成分及高频噪声三部分,在分解过程中,信号长度没有减少,没有信息的丢失;将分解得到的故障成分单独提取出来进行分析,可以更准确描述故障特征;对实际碰摩故障信号进行形态学分解后,提取出故障成分,采用Hilbert-Huang变换(Hilbert-Huang transform,HHT)对分解前后的信号进行对比分析,验证了方法的有效性,表明基于形态变换的信号特征提取可以更准确刻画故障的非平稳特性,提高了分析效果,并具有计算简单、快速的优点.     

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。     

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.     

Enhanced detection of rolling element bearing fault based on stochastic resonance

Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.     

Wheel-bearing fault diagnosis of trains using empirical wavelet transform

Rolling bearings are used widely as wheel bearing in trains. Fault detection of the wheel-bearing is of great significance to maintain the safety and comfort of train. Vibration signal analysis is the most popular technique that is used for rolling element bearing monitoring, however, the application of vibration signal analysis for wheel bearings is quite limited in practice. In this paper, a novel method called empirical wavelet transform (EWT) is used for the vibration signal analysis and fault diagnosis of wheel-bearing. The EWT method combines the classic wavelet with the empirical mode decomposition, which is suitable for the non-stationary vibration signals. The effectiveness of the method is validated using both simulated signals and the real wheel-bearing vibration signals. The results show that the EWT provides a good performance in the detection of outer race fault, roller fault, and the compound fault of outer race and roller.     

自适应无参经验小波变换及其在转子故障诊断中的应用

为了实现经验小波变换中Fourier谱的自适应分割,提出了自适应无参经验小波变换(APEWT)方法。同时,为了克服希尔伯特变换解调的不足,更精确地估计信号的时频分布,提出了改进归一化希尔伯特变换(INHT)。通过分析仿真信号将APEWT和INHT方法与经验模态分解(EMD)、总体平均经验模态分解(EEMD)和局部特征尺度分解等方法进行对比,结果表明了APEWT和INHT方法的优越性。最后,将基于APEWT和INHT的时频分析方法应用于转子局部碰磨故障诊断,试验数据分析结果表明,所提出的方法不仅能够有效地诊断转子局部碰磨故障,而且诊断效果优于EMD和EEMD方法。     

基于双时域微弱故障特征增强的轴承早期故障智能识别*

针对轴承早期微弱故障难以准确识别的问题,提出一种基于双时域微弱故障特征增强的轴承早期故障智能识别方法。利用广义S变换和Fourier逆变换推导出一种双时域变换,将轴承振动信号变换为双时域二维时间序列。根据双时域变换的能量分布特点,提取二维时间序列的主对角元素以构建故障特征增强的时域振动信号。仿真信号和轴承故障信号分析验证了双时域微弱故障特征增强的可行性和有效性。采用脉冲耦合神经网络和支持向量机对增强后的轴承信号进行时频特征参数提取和智能识别,平均识别精度达到了95.4%。试验结果表明所提方法能有效提高轴承早期故障的智能识别精度。     

基于 VMD 的数控机床旋转机械运行路径偏离故障检测方法

为精准提取数控机床旋转机械设备故障信息,量化数控机床旋转机械运行路径偏离程度,提出一种基于 VMD 的旋转机械运行路径偏离故障检测方法。分析数控机床旋转机械设备运行频率和振动情况,运用突变检测算法优化采集效率,使用自适应脉冲法采样机械信号;创建约束变分模型,利用遗传算法搜索信号变量最优值,通过 VMD 法分离信号频域分量,提取机械信号故障特征;通过聚类法评估路径偏离水平,构建胶囊网络进行路径偏离故障检测,利用 squash 函数挤压处理胶囊矢量并提升矢量维度,运用特征编码和归一化处理获得高精度偏离故障检测输出值。实验结果表明,所提方法检测的数控机床旋转机械运行路径偏离故障效果较好,且检测效率较高。     

Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The fault diagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, fault diagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A novel method for modelling the vibration signal based on the adaptive wavelet transform (AWT) is proposed. The linear combination of wavelets is introduced as wavelet itself and adapted for the particular vibration signal, which goes beyond adapting parameters of a fixed-shape wavelet. The AWT procedure based on the parametric optimisation by genetic algorithm (GA) is developed. The model-based method by AWT is applied to extract the features in the fault diagnosis of the water hydraulic motor. This technique for de-noising the corrupted simulation signal shows that it can improve the signal-to-noise ratio of the vibration signal. The results of the experimental signal demonstrate the characteristic vibration signal details in fine resolution. The magnitude plots of the continuous wavelet transform (CWT) show the characteristic signal's energy in time and frequency domain which can be used as feature values for fault diagnosis of water hydraulic motor.