基于双树复小波变换的心电信号去噪研究

在心电信号处理过程中,为了避免产生Gibbs振荡现象和严重的频率混叠现象,提出一种基于双树复小波变换,并结合最大后验估计确定阈值的心电信号去噪方法.文中采用了信噪比和均方误差来评价双树复小波变换和离散小波变换两种方法对心电信号的去噪效果.实验结果表明:与传统离散小波变换相比,双树复小波变换去噪更彻底,边界、纹理等特征能较好地保留,可以作为一种生物医学信号降噪处理的新方法.     

小波变换在旋转机械故障检测中的应用研究

详述了Fourier变换与小波变换的本质区别,分析了Fourier变换和短时Fourier变换应用于故障检测的不足;介绍了小波变换及其应用于故障检测的优点;指出了小波变换应用于故障检测的理论和方法。     

Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis

This paper proposes a multiscale slope feature extraction method using wavelet-based multiresolution analysis for rotating machinery fault diagnosis. The new method mainly includes three following steps: the discrete wavelet transform (DWT) is first performed on vibration signals gathered by accelerometer from rotating machinery to achieve a series of detailed signals at different scales; the variances of multiscale detailed signals are then calculated; finally, the wavelet-based multiscale slope features are estimated from the slope of logarithmic variances. The presented features reveal an inherent structure within the power spectra of vibration signals. The effectiveness of the proposed feature was verified by two experiments on bearing defect identification and gear wear diagnosis. Experimental results show that the wavelet-based multiscale slope features have the merits of high accuracy and stability in classifying different conditions of both bearings and gearbox, and thus are valuable for machinery fault diagnosis.     

小波算法在旋转机械故障诊断系统中的应用

目前旋转机械在社会各个方面均有着广泛的应用,同时旋转机械的故障也十分普遍。一旦发生故障就会造成巨大的经济损失。该文基于小波算法对旋转机械故障特征进行提取,在时域和频域中对提取数据进行分析处理,在上位中对故障诊断结果进行显示。以Visual Studio 2010为开发平台,C#为开发语言,运用小波算法,最终完成了故障诊断系统的开发。验证结果表明,该诊断系统具有诊断精度高、速度快等优点。     

Gear fault identification and localization using analytic wavelet transform of vibration signal

The aim of this present work is to identify and localize the defect in gear and measure the angle between two damaged teeth in the time domain of the vibration signal. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis. The enveloping technique is revisited for defect identification but is found unsatisfactory in measuring the angle between two faulty teeth. A signal processing scheme is proposed to filter the noise and to measure the angle between two damaged teeth. The proposed technique consists of undecimated wavelet transform (UWT), which is used to denoise the signal. The analytic wavelet transform (AWT) has been implemented on approximation signal followed by a time marginal integration (TMI) of the AWT scalogram. The TMI graph time-axis is mapped onto the angular displacement of the driver gear. The measurement is shown to identify the first and the second defective teeth impact on gear meshing, which is visible as sharp spikes in the TMI graph. An attempt is also made to replace the approximation from UWT with Intrinsic Mode Function (IMF) derived from the Empirical Mode Decomposition (EMD). The present experimental work establishes the proposed method of measuring and localizing multiple gear teeth defect using vibration signal in the time domain.     

Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet

Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive deep belief network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods.     

基于双树复数小波和SVR的红外小目标检测

在分析红外图像弱小目标和背景特征的基础上,提出了基于双树复数小波变换(dual-tree complex wavelet transform,DT-CWT)和支持向量回归(support vectorr egression,SVR)的检测方法。首先采用双树复数小波变换抑制大部分背景噪声;其次用SVR对去噪后的红外图像进行背景预测,并用去噪后的实际图像减去预测图像得到残差图像,大大提高了图像的信噪比;接着提出了基于模糊Tsallis-Havrda-Charvat熵的阈值选取算法,对残差图像进行阈值分割;最后根据目标的连续性和运动轨迹的一致性检测出真实的小目标。实验结果表明:该方法可显著提高红外目标的检测概率,实现较远距离弱小目标的检测。     

基于离散小波变换的机械故障信号提取

机械传动的轴承、齿轮等关键部位的故障信号中都含有冲击信息,通过对冲击信息的提取就可以对设备做出精密诊断.本文针对机械故障难以预先发现的问题,将离散小波变换和频谱分析相结合从机械的振动信号中提取非平稳信号,以此作为判断故障信号及类型的依据.从实际的应用效果看,利用离散小波变换技术提取冲击信号是非常有效的.     

Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks

This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.     

Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

This paper presents a novel method for fault diagnosis based on an improved wavelet package transform (IWPT), a distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, with investigating the feature of impact fault in vibration signals, a biorthogonal wavelet with impact property is constructed via lifting scheme, and the IWPT is carried out to extract salient frequency-band features from raw vibration signals. Then, the faulty features can be detected by envelope spectrum analysis of wavelet package coefficients of the most salient frequency band. Secondly, with the distance evaluation technique, the optimal features are selected from the statistical characteristics of raw signals and wavelet package coefficients, and the energy characteristics of decomposition frequency band. Finally, the optimal features are input into the SVMs ensemble with AdaBoost algorithm to identify the different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the SVMs ensemble can reliably separate different fault conditions and identify the severity of incipient faults, which has a better classification performance compared to the single SVMs.     

Weak feature enhancement in machinery fault diagnosis using empirical wavelet transform and an improved adaptive bistable stochastic resonance

Machinery vibration signal is a typical multi-component signal and fault features are often submerged by some interference components. To accurately extract fault features, a weak feature enhancement method based on empirical wavelet transform (EWT) and an improved adaptive bistable stochastic resonance (IABSR) is proposed. This method makes full use of the signal decomposition performance of EWT and the signal enhancement of the IABSR to achieve the purpose of fault feature enhancement in low frequency band of FFT spectrum. Firstly, EWT is used as the preprocessing program of bistable stochastic resonance (BSR) to decompose the machinery vibration signal into a set of sub-components. Then, the sensitive component that contains main fault information is further input into BSR system to enhance fault features with the assistance of residual noises. Finally, the fault features are identified from fast Fourier transform (FFT) spectrum of the BSR output. To achieve the optimal BSR output, the IABSR method based on salp swarm algorithm (SSA) is presented. Compared with the tradition adaptive BSR (ABSR), the IABSR optimizes not only the BSR system parameters but also the calculation step size. Two case studies on machinery fault diagnosis demonstrate the effectiveness and superiority of the proposed method. In addition, the proposed method is easy to implement and is robust to noise to some extent.     

Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques

Time-frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.     

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.     

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.     

Detection of weak fault using sparse empirical wavelet transform for cyclic fault

The successful prediction of the remaining useful life of rolling element bearings depends on the capability of early fault detection. A critical step in fault diagnosis is to use the correct signal processing techniques to extract the fault signal. This paper proposes a newly developed diagnostic model using a sparse-based empirical wavelet transform (EWT) to enhance the fault signal to noise ratio. The unprocessed signal is first analyzed using the kurtogram to locate the fault frequency band and filter out the system noise. Then, the preprocessed signal is filtered using the EWT. The l_q-regularized sparse regression is implemented to obtain a sparse solution of the defect signal in the frequency domain. The proposed method demonstrates a significant improvement of the signal to noise ratio and is applicable for detection of cyclic fault, which includes the extraction of the fault signatures of bearings and gearboxes.     

基于小波变换的声波法测油井动液面信号去噪

由于受到油套环空中各种噪声的影响,用声波法测油井动液面过程中,得到的反射声波信号异常复杂,常常出现液面反射位置不容易辨识的情况.该文分析了噪声产生的原因,将小波阈值去噪的方法用于声波法测油井动液面信号的噪声去除.结果显示,利用小波去噪方法处理油井动液面信号能够有效地去除噪声,随着小波分解层数的增加,利用小波方法去噪的效...     

Fault diagnosis of partial rub and looseness in rotating machinery using Hilbert-Huang transform

Partial rub and looseness are common faults in rotating machinery because of the clearance between the rotor and the stator. These problems cause malfunctions in rotating machinery and create strange vibrations coming from impact and friction. However, non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency information is needed for identifying these signals. For this purpose, a newly developed time-frequency analysis method, HHT (Hilbert-Huang Transform), is applied to the signals of partial rub and looseness from the experiment using RK-4 rotor kit. Conventional signal processing methods such as FFT, STFT and CWT were compared to verify the effectiveness of fault diagnosis using HHT. The results showed that the impact signals were generated regularly when partial rub occurred, but the intermittent impact and friction signals were generated irregularly when looseness occurred. The time and frequency information was represented exactly by using HHT in both cases, which makes clear fault diagnosis between partial rub and looseness. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin     

基于BEMD与双树复小波-局部Wiener滤波的毫米波图像去噪

为了有效地去除毫米波图像中含有的噪声,提高目标识别的精度,提出一种将二维经验模式分解(BEMD,Bidimensional Empirical Mode Decomposition)与基于双树复小波变换(DTCWT)的加窗局部Wiener滤波相结合的图像去噪算法。首先,对毫米波图像进行BEMD分解,得到不同特征尺度的本征模函数(IMF,Intrinsic Mode Function)子图像集;其次,利用双树复小波变换对中高频IMF子图像进行多尺度、多方向分解,并结合带有椭圆方向窗的局部Wiener滤波算法对各个高频方向子带进行去噪;最后通过DTCWT逆变换重构得到去噪后的IMF,并与残差图像相加进行BEMD重构。实验结果表明,该融合算法与单独的BEMD,DTCWT-Wiener滤波及离散小波变换-Wiener滤波算法相比,去噪后图像的视觉效果更好,提取的目标的边缘及细节特征更清晰,因而峰值信噪比最高。     

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.