SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

Golafshan, Reza; Şanlıtürk, Kenan Y.

doi:10.1016/j.ymssp.2015.08.012

Cited by 144 publications

(79 citation statements)

References 29 publications

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“…Generally, amplitude-modulated and/or frequency-modulated (AM-FM) multi-component signals collected by sensors are interfered with by background noise, resulting lower signal-to-noise ratio (SNR) [1]. The reason for the low SNR signals obtained by a determinate acquisition system are usually as follows: firstly, when a rolling bearing is in the infantile fault period, the fault feature is inconspicuous and buried in the background noise.…”

Section: Introductionmentioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction.

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Section: Introductionmentioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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“…They generally operate in tough working environments and are easily subject to failures, which may cause machinery to break down and decrease machinery service performance such as manufacturing quality or operation safety [1][2][3][4]. When a defect occurs on a rolling bearing surface, the impulses are created in vibration signals [5,6]. As a result, the detection of faults in rolling element bearings is mainly achieved by identifying the frequency of the impulses from the signals [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…For complicated mechanical systems, the rolling bearing often works in complicated environments, and the vibration signals are easily contaminated by environmental noise and other working parts such as the gearbox (misalignment, unbalance, crack on the rotating shaft, looseness, and distortions). Therefore, their early impulse faults often feature weak and low signal to noise ratios (SNR) [5,6].…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

Zheng

Zhang

et al. 2017

Applied Sciences

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Abstract:The periodic impulse feature is the most typical fault signature of the vibration signal from fault rolling element bearings (REBs). However, it is easily contaminated by noise and interference harmonics. In order to extract the incipient impulse feature from the fault vibration signal, this paper presented an autocorrelation function periodic impulse harmonic to noise ratio (ACFHNR) index based on the SVD-Teager energy operator (TEO) method. Firstly, the Hankel matrix is constructed based on the raw vibration fault signal of rolling bearing, and the SVD method is used to obtain the singular components. Afterwards, the ACFHNR index is employed to measure the abundance of the periodic impulse fault feature for the singular components, and the component with the largest ACFHNR index value is extracted. Moreover, the properties of the ACFHNR index are demonstrated by simulations and the full life cycle of the experiment, showing its superiority over the traditional kurtosis and root mean square (RMS) index for extracting and detecting incipient periodic impulse features. Finally, the Teager energy operator spectrum of the extracted informative signal is gained. The simulation and experimental results indicated that the proposed ACFHNR index based method can effectively detect the incipient fault feature of the rolling bearing, and it shows better performance than the kurtosis and RMS index based methods.Keywords: rolling element bearings (REBs); singular value decomposition (SVD); autocorrelation function impulse harmonic to noise ratio (ACFHNR); teager energy operator (TEO)

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“…用。如在轴承振动信号 [7][8] 、语音信号 [9] 、电荷放电信号 [10] 等不同性质信号的消噪方面，这种矩阵形式都可以取得相当好的消噪效果。本文拟不再探讨 SVD 的消噪问题，而是提出利用 SVD 实现信号中不同频率的分离，这与通常的 SVD 消噪应用完全不同，因此不能再采用 Hankel 矩阵。为达到信号分离的目的，我们采用另一种矩阵形式：对于一个待处理信号，将信号均分成多段，利用每段形成矩阵的行向量。这种矩阵在信号处理中也有一定的应用，如 KANJILAL 等 [11][12] 利用这种矩阵通过奇异值比谱来检测信号的周期性，并从复合母体心电信号中提取胎儿心电信息，再如 CONG 等 [13] 将 SVD 用于信号处理时首先必须利用信号构造合适的矩阵 A。采用 Hankel 矩阵时可以消除信号中的噪声 [7][8][9][10] ，但是我们的目的是实现信号的分离，因此必须另外构造矩阵。对于一个数字信号序列 X=(x(1)，x(2)， … ，x(N))，取两个正整数 m 和 n，对此序列按每次 n 个点连续截取 m 段，以这 m 段构造矩阵 A 如下 (1) (2) ( ) ( 1) ( 2) …”

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“…) 尽管这种矩阵在信号处理和故障诊断中已有一定的应用 [11][12][13] ，但是简单地采用这种矩阵并不能实 T 1 2 1 1 2 1 2 1 2 T 1 2 2 1 2 1 2 , , , , , , , [7][8][9][10] ，此外，也可采用小波变换方法来消除噪声 [18] 。 (2) 为了使变结构 SVD 算法获得良好的频率分离效果，必须对信号进行整周期采样，这是由算法的信号分离机理决定的。…”

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Variable Structure SVD Algorithm and Its Application to Signal Separation

Zhao¹

2017

JME

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Abstract：The key question of applying singular value decomposition (SVD) to signal processing is the construction of the matrix. In order to separate the different frequency components from the original signal through SVD, a recursive SVD algorithm with variable matrix structure is proposed, whose idea is to change the structure of the matrix in the process of SVD recursion decomposition, each time when the SVD is carried out, the structure of the matrix to be decomposed will change regularly to adapt to the different frequency components in the signal, so that the different frequency components can be separated. The signal decomposition algorithm of the variable structure SVD is deduced, and it is proved that the original signal can be decomposed into a linear combination of a series of component signals by this algorithm. Furthermore, the signal separation mechanism of this algorithm is analyzed theoretically, and it is proved that for some frequency structure, variable structure SVD can separate the each single frequency component successively from the original signal. Finally, the separation examples of the simulation signal and actual engineering signal are provided, which demonstrate the good signal separation effect of the variable structure SVD algorithm. The comparison with wavelet analysis and multi-resolution SVD method shows that variable structure SVD can achieve the better signal separation results than these two methods.

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SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

Cited by 144 publications

References 29 publications

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Incipient Fault Feature Extraction of Rolling Bearings Using Autocorrelation Function Impulse Harmonic to Noise Ratio Index Based SVD and Teager Energy Operator

Variable Structure SVD Algorithm and Its Application to Signal Separation

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