scholarly journals A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Cancan Yi ◽  
Yong Lv ◽  
Zhang Dang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Pso Algorithm ◽  
Potential Method ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Mode Decomposition

Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

scholarly journals Rolling Bearing Fault Signal Extraction Based on Stochastic Resonance-Based Denoising and VMD

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaojiao Gu ◽  
Changzheng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Stochastic Resonance ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Signal Extraction ◽  
Resonance System ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Aiming at the difficulty of early fault vibration signal extraction of rolling bearing, a method of fault weak signal extraction based on variational mode decomposition (VMD) and quantum particle swarm optimization adaptive stochastic resonance (QPSO-SR) for denoising is proposed. Firstly, stochastic resonance parameters are optimized adaptively by using quantum particle swarm optimization algorithm according to the characteristics of the original fault vibration signal. The best stochastic resonance system parameters are output when the signal to noise ratio reaches the maximum value. Secondly, the original signal is processed by optimal stochastic resonance system for denoising. The influence of the noise interference and the impact component on the results is weakened. The amplitude of the fault signal is enhanced. Then the VMD method is used to decompose the denoised signal to realize the extraction of fault weak signals. The proposed method was applied in simulated fault signals and actual fault signals. The results show that the proposed method can reduce the effect of noise and improve the computational accuracy of VMD in noise background. It makes VMD more effective in the field of fault diagnosis. The proposed method is helpful to realize the accurate diagnosis of rolling bearing early fault.

Particle Swarm Optimization and Adaptive Wavelet Theory for Vibration Signal Application in Fault Diagnosis of Gearbox System

2013 ◽  
Vol 791-793 ◽  
pp. 958-961
Author(s):  
Han Xin Chen ◽  
Yan Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Pso Algorithm ◽  
Economic Losses ◽  
Early Age ◽  
Wavelet Theory ◽  
Swarm Optimization ◽  
Adaptive Wavelet ◽  
Novel Method

Gearbox system is widely used in mechanical industry,but serious failure is always occurred in the gearbox system. So it is very necessary to diagnose the fault of gearbox in the early-age avoiding economic losses. In this paper, a novel method for extracting the characteristic information from the vibration signal of gearbox system based on the particle swarm optimization (PSO) algorithm and adaptive wavelet theory is proposed.

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

2021 ◽  
pp. 014233122198900
Author(s):  
Jingyi Lu ◽  
Xue Qu ◽  
Dongmei Wang ◽  
Jikang Yue ◽  
Lijuan Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Noise Reduction ◽  
Euclidean Distance ◽  
Particle Swarm ◽  
Variational Mode Decomposition ◽  
Swarm Optimization ◽  
Signal Filtering ◽  
Mode Decomposition ◽  
Mean And Variance ◽  
The Mean

In order to deal with the problem that the noise of leakage signals from natural gas pipelines has a great influence on the feature extraction of pipeline leakage, this paper proposes a signal denoising method of variational mode decomposition (VMD) and Euclidean distance (ED) based on optimizing parameters of classification particle swarm optimization (CPSO) algorithm. First, CPSO algorithm is used to optimize parameters K and [Formula: see text] of VMD, adaptively. The sum of the ratio of the mean and variance of the cross-correlation coefficient and the ratio of the mean and variance of kurtosis is used as the fitness function of CPSO. Then, the optimized VMD is used to decompose the signal to obtain several intrinsic mode functions (IMFs). Finally, ED is used to select the effective modes, and the signal is reconstructed to achieve signal noise reduction. The corresponding evaluation indicators show that the accuracy and robustness of the improved method are better than other noise reduction methods. The denoising effect is significant, which proves that the algorithm proposed in this paper is effective in signal filtering.

scholarly journals Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 920 ◽  
Author(s):  
Yong Lv ◽  
Yi Zhang ◽  
Cancan Yi
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Filtering Algorithm ◽  
Number Of Components ◽  
Iterative Filtering

The characteristics of the early fault signal of the rolling bearing are weak and this leads to difficulties in feature extraction. In order to diagnose and identify the fault feature from the bearing vibration signal, an adaptive local iterative filter decomposition method based on permutation entropy is proposed in this paper. As a new time-frequency analysis method, the adaptive local iterative filtering overcomes two main problems of mode decomposition, comparing traditional methods: modal aliasing and the number of components is uncertain. However, there are still some problems in adaptive local iterative filtering, mainly the selection of threshold parameters and the number of components. In this paper, an improved adaptive local iterative filtering algorithm based on particle swarm optimization and permutation entropy is proposed. Firstly, particle swarm optimization is applied to select threshold parameters and the number of components in ALIF. Then, permutation entropy is used to evaluate the mode components we desire. In order to verify the effectiveness of the proposed method, the numerical simulation and experimental data of bearing failure are analyzed.

scholarly journals Particle Swarm Optimization Algorithm-Based Design Method for Ultrasonic Transducers

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 715
Author(s):  
Dongdong Chen ◽  
Jianxin Zhao ◽  
Chunlong Fei ◽  
Di Li ◽  
Yuanbo Zhu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Equivalent Circuit ◽  
Design Method ◽  
Particle Swarm ◽  
Equivalent Circuit Model ◽  
Pso Algorithm ◽  
Circuit Model ◽  
Center Frequency ◽  
Swarm Optimization ◽  
And Performance

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1−x)O3 (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

Sign in / Sign up

Export Citation Format

Share Document