A repeated single-channel mechanical signal blind separation method based on morphological filtering and singular value decomposition

This paper proposes a repeated blind source separation (BSS) method based on morphological filtering and singular value decomposition (SVD) to separate the mixed sources from a single-channel signal. Firstly the signal is de-noised by the morphological filter and, the noise which affects the accuracy of the separation is removed. Next, the purified signal is reconstructed in phase space, and the SVD is applied to this matrix. After choosing the effective singular values, the inverse transform is applied to the revised signal matrix. From this, the pseudo signal can be obtained. The pseudo signal and the purified original signal are used to achieve the mixed sources separation through the fast independent component analysis (FastICA) algorithm. Then, the methods above are repeated in order to separate the weaker signals. The analysis of simulation and practical application demonstrates that that proposed method shows a high level of separating performance of a single-channel signal.     

De-noising of diesel vibration signal using wavelet packet and singular value decomposition

The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise. __________ Translated from Journal of China University of Petroleum (Natural Science Edition), 2006, 30(1) (in Chinese)     

Condition monitoring of engine timing system by using wavelet packet decomposition of a acoustic signal

A change in the technical condition of mechanical components of internal combustion engines may not be detected by on-board diagnostic systems installed in vehicles. In similar cases, measurements and analyses of vibroacoustic signals being recorded prove useful. Since there are certain limitations to vibration measurements, including those related to the vibration transmission and the engine’s high temperature at measurement points, the authors of this paper have proposed that measurements and analyses of acoustic signals should be applied for the sake of assessment of the internal combustion engine technical condition. However, such an assessment requires new acoustic signal processing methods to be developed, and so this subject has been elaborated in the paper. The article provides a discussion on the option of applying a wavelet packet decomposition while filtering the internal combustion engine’s acoustic signal in order to diagnose an excessive valve clearance. The authors prepared an algorithm enabling selection of the chosen details and approximation of the wavelet analysis to low-frequency components, which constitute the noise, as well as high-frequency components comprising information on the possible enlarged engine valve clearance. Next, based on the selected high-frequency acoustic signal components, a method for automatic detection of enlarged clearance valves was developed, assuming that energy participations of the acoustic signal being emitted were to be determined while opening and closing individual valves. Under the study discussed, identification tests were conducted using two 4-cylinder internal combustion engines featuring valves of different clearances to consequently confirm the efficiency of the algorithm developed for the acoustic signal filtration and automatic detection of enlarged clearance valves.     

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.     

并联基追踪稀疏分解在齿轮箱弱故障诊断中的应用

为了提取齿轮箱振动信号淹没在强背景噪声中的早期微弱冲击故障信息,先利用不同长度的窗函数的短时傅里叶变换对信号进行稀疏分解,得出初始分解系数,再利用并联基追踪对处理得到的系数进行优化处理,最后对得到的系数进行重构,分别得到信号的持续振荡成分及故障冲击成分,进一步对故障冲击成分分析得出诊断结果。仿真信号分析及应用实例分析结果表明了算法的可行性及有效性,为强噪声环境下的机械故障信号提取提供了一种新的思路。     

基于改进谐波小波包分解的滚动轴承复合故障特征分离方法

为解决滚动轴承单通道振动信号中复合故障特征难以分离的问题,提出了基于改进谐波小波包分解的轴承复合故障特征分离方法。首先,改进了二进谐波小波包分解方法,提出了连续谐波小波包分解方法,克服了信号分解后子带个数和带宽范围受二进制划分的缺陷;然后,采用谐波窗分解提取信号中频率成分集中的频段,根据轴承各单点故障特征频率确定分解层数,进行连续谐波小波包分解,利用能量算子包络解调得到子带信号中各个单点故障的权重因子;最后,重构轴承各单点故障信号,实现复合故障的特征分离和提取。对仿真信号和实测轴承内、外圈复合故障信号分析的结果表明,该方法能将轴承单通道复合故障信号分解到不同的通道中,实现了复合故障特征的分离,具有一定的工程实用价值。     

Separation method of rolling bearing compound fault characteristics based on Improved harmonic wavelet packet decomposition and fast ICA

Compound fault characteristics in single-channel vibration signals of rolling bearings are difficult to separate. On the basis of improved harmonic wavelet packet decomposition and fast independent component analysis (FICA), this study proposes a new method to address this problem. First, a series of mutually independent frequency bands are obtained after harmonic wavelet packet decomposition of the initial vibration signal to satisfy the requirement that the number of observed signals must be larger than the number of source signals in the FICA algorithm. Second, the optimal frequency bands are selected based on the maximum kurtosis index and used as the input matrix of the FICA algorithm to separate the compound fault characteristics further. Lastly, accurate separation and extraction of the compound fault characteristics of the rolling bearings are realized. Results show that the proposed method can effectively separate the compound fault characteristics in the single-channel vibration signals of the bearings.

     

Biphase randomization wavelet bicoherence for mechanical fault diagnosis

Wavelet bicoherence is one of the most useful tools for quadratic nonlinear behavior identification of stochastic system, which has been used in many fields. However, current wavelet bicoherence algorithm can neither eliminate the spurious peaks coming from components with long coherence time, nor distinguish the quadratic phase coupling and non quadratic phase coupling signals, which may constraint the application of wavelet bicoherence. In this article, biphase randomization wavelet bicoherence technique is proposed to solve this problem. In this method, an ensemble average biphase randomization algorithm is established, in which the biphase randomization is employed to damage the biphase dependence among bispectrum samples. The spurious bicoherence coming from long coherence time waves and non phase coupling waves is eliminated efficiently by using the proposed method. Based on that, two diagnosis features are established for mechanical fault diagnosis. Simulation and experiment results demonstrate that the performance of the proposed method is much better than that of current wavelet bicoherence method.     

Detection of dressing time using the grinding force signal based on the discrete wavelet decomposition

This paper describes the use of the grinding force signal to show the mechanical noise reduction and to detect the dressing time based on the discrete wavelet decomposition. As a result of de-noising, the wavelet de-noising method was more effective than the FFT filtering technique. From the approximation coefficients of the higher order wavelet transform, the grinding force signal obtained by a tool dynamometer was clear so it was possible to successfully detect the dressing time. A measured result by the surface roughness and the ground surface photograph coincided with the detection result.     

Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor based on bispectrum analysis. The theoretical basis is developed to understand the nonlinear characteristics of current signals when the motor undertakes a varying load under different faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is then adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signal more accurately. Based on this new bispectrum analysis a more effective diagnostic feature, namely normalised bispectral peak, is developed for fault classification. In association with the kurtosis value of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from the other fault cases and different degrees of discharge valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.     

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.     

一种综合小波变换的心电信号消噪算法

针对心电信号中混有的基线漂移、工频干扰、肌电干扰等噪声,比较了适于心电信号的4种基于小波变换的心电信号消噪算法,结合消噪后的信噪比和信号失真度,提出一种综合小波变换的心电信号消噪算法.该算法先使用小波分解法消除心电信号中的基线漂移,再利用模极大值法消除工频干扰、肌电干扰等噪声.并且运用该算法对MIT-BIH心律失常数据库中的含有多种噪声的心电数据进行了仿真与实验,结果表明噪声被有效地消除并且失真度较小,可满足临床分析与诊断对心电波形的要求.     

基于小波奇异性的机械故障检测

介绍了小波变换的基本原理及小波奇异性用于机械故障检测的基本原理,提出了一种基于小波奇异性的机械故障检测方法,并根据小波变换模极大值在不同尺度下的分布来完成故障的检测。仿真实验证实了该方法的可行性。     

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.     

高斯混合模型与小波包能量相结合的齿轮故障分类

提出小波包能量与高斯混合模型相结合的齿轮故障分类算法。利用小波包分析提取某种模式下齿轮振动信号多层分解后的不同频带内的能量,并进行归一化处理。然后以各频带能量为元素构造该模式的特征向量,利用这些特征向量以及高斯混合模型良好的数据分布刻画能力,对该模式进行描述。最后采用贝叶斯分类器进行齿轮故障分类。采用该方法对齿轮振动信号进行故障识别,结果表明能取得比人工神经网络算法更高的识别率。     

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.     

Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.     

小波包原理在滚动轴承声发射检测技术中的应用

详细分析了小波滤波原理。在大量实验分析和理论分析的基础上，提出了使用基于特征频率的小波滤波方法。从而解决了由噪声引起的检测故障滚动轴承误判的问题，并为下一步的信号特征提取打下了坚实的基础。     

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.     

Linear signal distortion compensation using harmonic decomposition

Compensating for linear distortions of a signal under uniform sampling using Fourier series is considered.