基于迭代Hilbert变换的多分量信号解调方法研究及应用

旋转机械系统发生故障时,其振动信号通常为多分量AM-FM信号.针对传统的解调方法在多分量振动信号故障特征提取中的局限性,提出一种利用迭代Hilbert变换(Iterated Hilbert transform,IHT)进行机械故障诊断的新方法.介绍IHT的基本原理;通过对任一两分量的AM-FM信号的分析表明利用IHT得到的相位信息直接估计瞬时频率具有一定的局限性,于是提出基于差分算和零相位数字低通滤波的平滑的瞬时频率估计方法,并通过仿真试验表明,与自适应分割算法和Hilbert-Hnang变换相比,该方法具有很高的精度且速度较快.对具有外圈故障的滚动轴承和具有断齿故障的齿轮箱振动信号的分析结果表明,基于IHT的多分量AM-FM信号解调方法能有效地提取机械故障振动信号中的故障特征.     

IMPROVED METHOD FOR HILBERT INSTANTANEOUS FREQUENCY ESTIMATION

In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency (IF) character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analytical signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.     

FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.     

变分模态分解在转子故障诊断中的应用

针对转子故障诊断问题,提出一种基于变分模态分解(variational mode decomposition,简称VMD)的信号处理方法。该方法在获取分解分量的过程中通过迭代搜寻变分模型最优解来确定每个分量的频率中心及带宽,从而能够自适应地实现信号的频域剖分及各分量的有效分离,对各单分量信号进行希尔伯特变换,即可得到瞬时的频率和幅值信息。对仿真信号和典型转子故障信号进行VMD方法和经验模态分解(empirical mode decomposition,简称EMD)方法的分析比较,以验证所提方法的有效性。仿真信号的分解结果表明,变分模态能够准确分离出信号中的固有模态分量且不存在模态混叠;转子故障实验信号的分析结果表明,所提方法能够有效提取出明显的故障特征,从而准确诊断出转子存在的故障。     

航空发动机试验传感器温漂数据有效性验证

针对航空发动机在台架试车中出现的传感器温漂故障,提出了基于经验模式分解(EMD)和Hilbert变换的航空发动机传感器数据有效性验证方法。首先,介绍了航空发动机传感器常见的失效模式以及EMD分解和Hilbert变换方法,并将其引入航空发动机传感器信号分析领域;然后,利用该方法对传感器故障信号进行分解,提取航空发动机传感器故障信号特征,通过故障信号重构和残差向量分析判断故障的严重程度,将原始信号中的故障信号予以剔除;最后,重构有效的信息成分,实现对数据的有效性验证。实例计算与分析验证了该方法在航空发动机传感器温漂数据有效性验证方面是有效的。     

A Feature Extraction Method for the Wear of Milling Tools Based on the Hilbert Marginal Spectrum

Abstract

The Hilbert–Huang transform (HHT) can adaptively delineate complex non-linear, non-stationary signals when used as the Hilbert–Huang marginal spectrum through empirical mode decomposition (EMD) and the Hilbert transform, to highlight local features of signals. Characterized by high resolution, the Hilbert marginal spectrum has been widely applied in mechanical signal processing and fault diagnosis. In the research, an HHT based on the improved EMD was proposed to analyze the cutting force, vibration acceleration (AC), and acoustic emission (AE) signals during tool wear in the milling process. At first, the collected signals were subjected to range analysis, which revealed that tool wear was closely related to the signals collected during the cutting process. Then, EMD was applied to the signals, followed by variance analysis after calculating the energies of each intrinsic mode function (IMF) component. Afterwards, the IMF components significantly influenced by wear degree, while slightly influenced by the three cutting factors (cutting velocity, feed per tooth, and cutting depth), were selected as IMF sensitive to the degree of wear. The HHT was finally applied to the sensitive IMF components of signals containing major tool wear information, thus obtaining the Hilbert marginal spectra of the signals, which were able to reflect the changes in signal amplitude with frequency. On the basis of the Hilbert marginal spectrum, the method defined the feature energy function which was then used as the eigenvector for predicting tool wear in milling processes. The analysis of signals in four tool wear states indicated that the method can extract salient tool wear features.     

Bearing fault detection utilizing group delay and the Hilbert-Huang transform

Vibration signals measured from a mechanical system are useful to detect system faults. Signal processing has been used to extract fault information in bearing systems. However, a wide vibration signal frequency band often affects the ability to obtain the effective fault features. In addition, a few oscillation components are not useful at the entire frequency band in a vibration signal. By contrast, useful fatigue information can be embedded in the noise oscillation components. Thus, a method to estimate which frequency band contains fault information utilizing group delay was proposed in this paper. Group delay as a measure of phase distortion can indicate the phase structure relationship in the frequency domain between original (with noise) and denoising signals. We used the empirical mode decomposition of a Hilbert-Huang transform to sift the useful intrinsic mode functions based on the results of group delay after determining the valuable frequency band. Finally, envelope analysis and the energy distribution after the Hilbert transform were used to complete the fault diagnosis. The practical bearing fault data, which were divided into inner and outer race faults, were used to verify the efficiency and quality of the proposed method.     

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.     

Gear fault identification based on Hilbert–Huang transform and SOM neural network

Gear vibration signals always display non-stationary behavior. HHT (Hilbert–Huang transform) is a method for adaptive analysis of non-linear and non-stationary signals, but it can only distinguish conspicuous faults. SOM (self-organizing feature map) neural network is a network learning with no instructors which has self-adaptive and self-learning features and can compensate for the disadvantage of HHT. This paper proposed a new gear fault identification method based on HHT and SOM neural network. Firstly, the frequency families of gear vibration signals were separated effectively by EMD (empirical mode decomposition). Then Hilbert spectrum and Hilbert marginal spectrum were obtained by Hilbert transform of IMFs (intrinsic mode functions). The amplitude changes of gear vibration signals along with time and frequency had been displayed respectively. After HHT, the energy percentage of the first six IMFs were chosen as input vectors of SOM neural network for fault classification. The analysis results showed that the fault features of these signals can be accurately extracted and distinguished with the proposed approach.     

Hilbert-Huang变换在滚动轴承故障诊断中的应用

提出了一种新的滚动轴承故障诊断方法——基于小波系数包络信号的局部Hilbert边际谱方法，在Hilbert—Huang变换的基础上介绍了局部Hilbert谱和局部Hilbert边际谱，并将它应用于滚动轴承的故障诊断中。用小波基将滚动轴承故障振动信号分解，对高频段的小波系数用Hilbert进行包络分析得到包络信号，再对包络信号进行Hilbert—Huang变换求出局部Hilbert边际谱，从局部Hilbert边际谱中就可以判断滚动轴承的故障部位和类型。通过对滚动轴承具有外圈缺陷、内圈缺陷的情况下的振动信号的分析，说明该方法比传统的包络分析方法更能有效地提取滚动轴承故障特征。