Application of an Improved Wavelet Threshold Denoising Method for Vibration Signal Processing

Xie, Zhi Jie; Song, Bao Yu; Zhang, Yang; Zhang, Feng

doi:10.4028/www.scientific.net/amr.889-890.799

Cited by 16 publications

(10 citation statements)

References 6 publications

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“…An effective noise reduction method for time-frequency analysis would improve time-frequency resolution and algorithm performance, which are required both for practical applications and theoretical analysis. Wavelet denoising is a vibration signal, Qin et al used the wavelet denoising method to improving the detection performance of distributed vibration sensor [1], and Xie et al proposed an improved wavelet denoise method for vibration signal processing [2]. Other methods proposed an improved EMD method for time-frequency feature extraction of telemetry vibration [3].…”

Section: Introductionmentioning

confidence: 99%

A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

2021

Shock and Vibration

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Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

2021

Shock and Vibration

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“…The engine has both rotational and reciprocating motion. Vibration signals are fully used in fault diagnosis because of their convenience [1,2]. The vibration signal of engine is composed of multi-component complex signals, and its amplitude varies with time.…”

Section: Introductionmentioning

confidence: 99%

An improved variational mode decomposition method and its application in diesel engine fault diagnosis

Ren¹,

Jin²,

Mei³

et al. 2018

J. vibroeng.

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The diesel engine is a complex mechanical device, with the characteristics of multi-source, multi moving parts, complex work. For the complex multi-component signal, it is usually necessary to decompose it into a number of single-component AM-FM signals, and each component is analyzed to extract amplitude and frequency information. VMD is essentially composed of a plurality of adaptive Wiener filter and has good noise robustness. Compared with EMD, EEMD, CEEMDAN, LMD and ITD, VMD has strong mathematical theory basis. At the same time, VMD rejects the method of recursive screening stripping. So VMD can effectively alleviate or avoid a series of problems which appear in other methods. However, it is a problem how to determine the number of decomposition layers and the penalty factor, because human factors will affect the decomposition results. In order to solve the problem, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the parameters of VMD. Genetic algorithms mainly include 3 genetic operators: selection, crossover and mutation. The cross probability and mutation probability will directly affect the optimization results. In the traditional genetic algorithm, the probability of cross and mutation are fixed, and the genetic algorithm is easy to fall into the local optimal. According to the regulation of hormone regulation, the cross probability and mutation probability in evolution were improved. The permutation entropy is a new method of mutation detection, which mainly aims at the spatial characteristics of the time series itself. Therefore, the entropy of the components obtained by the VMD decomposition is used as the fitness function of the IAGA. The modal number and penalty factor of VMD were iteratively optimized by IAGA, and the optimal combination of parameters was obtained. Based on the proposed method, the vibration signals of the crankshaft bearing fault simulation experiment were decomposed into several components. According to the value of the permutation entropy, the fault components were selected and the energy was extracted. The fault pattern is identified by the support vector machine (SVM) successfully. The simulation analysis and the simulation experiment of the crankshaft bearing fault show that the proposed method is effective. For the diagnosis of other engines, a large number of validation experiments are needed for further research.

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“…However, mechanical vibration signal are usually masked by significant background noise, which have motivated many studies into developing denoising methods [1]. The vibration signals should be processed to reduce noise and improve the quality before further analyzing [2]. Many researchers in this field have made thorough explorations.…”

Section: Introductionmentioning

confidence: 99%

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Ren¹,

Jin²,

Mei³

et al. 2018

J. vibroeng.

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Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

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Application of an Improved Wavelet Threshold Denoising Method for Vibration Signal Processing

Cited by 16 publications

References 6 publications

A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

An improved variational mode decomposition method and its application in diesel engine fault diagnosis

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

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