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 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.

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 Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

2017 ◽  
Vol 36 (4) ◽  
pp. 354-365 ◽  
Author(s):  
Shaojiang Dong ◽  
Tianhong Luo ◽  
Li Zhong ◽  
Lili Chen ◽  
Xiangyang Xu
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Frequency Domain Method ◽  
Nearest Neighbour ◽  
Swarm Optimization ◽  
Time Frequency

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time–frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

scholarly journals Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Asrul Adam ◽  
Mohd Ibrahim Shapiai ◽  
Mohd Zaidi Mohd Tumari ◽  
Mohd Saberi Mohamad ◽  
Marizan Mubin
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Domain Analysis ◽  
Peak Detection ◽  
Parameters Estimation ◽  
Eeg Signals ◽  
Classification Rate ◽  
Swarm Optimization ◽  
Time Frequency

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Measurement While Drilling Mud Pulse Signal Denoising and Extraction Approach Based on Particle-Swarm-Optimized Time-Varying Filtering Empirical Mode Decomposition

2020 ◽  
pp. 1-11
Author(s):  
Xianyou Zhong ◽  
Li Cen ◽  
Yankun Zhao ◽  
Tianwei Huang ◽  
Jinjin Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Empirical Mode Decomposition ◽  
Drilling Fluid ◽  
Particle Swarm ◽  
Pulse Signal ◽  
Permutation Entropy ◽  
Time Varying ◽  
Swarm Optimization ◽  
Mode Decomposition ◽  
Reconstructed Signal

Summary At present, mud pulse transmission is widely used in underground wireless transmission. To extract more accurately the original drilling fluid pulse signals while drilling, in this paper, we developed an algorithm for optimal denoising shaping based on particle-swarm-optimized time-varying filtering empirical mode decomposition (TVFEMD). The performance of TVFEMD heavily depends on its parameters (i.e., B-spline order and bandwidth threshold). In the traditional TVFEMD method, the parameters are given in advance and may not be optimized, so it is difficult to achieve satisfactory decomposition results. To tackle this issue, the correlation coefficient was used as the objective function, and the particle-swarm-optimization algorithm was used to optimize the parameters of TVFEMD in this paper. First, the particle swarm optimization was used to search for the best combination of parameters. Then, the TVFEMD was applied to obtain a series of intrinsic mode functions (IMFs). Subsequently, the optimal denoising and shaping algorithm was used to determine the best reconstructed signal by low-pass filtering. Permutation entropy was taken as the evaluation index to obtain a reconstruction signal. Finally, the reconstructed signal was processed by square wave shaping to obtain accurate drilling fluid pulse signals. The approximation of the algorithm is 0.7581, and relevance is as high as 0.8535. The simulation signal and drilling fluid pulse signal analysis results showed that the proposed approach can extract the original pulse signal accurately.

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.

scholarly journals Refocusing High-Resolution SAR Images of Complex Moving Vessels Using Co-Evolutionary Particle Swarm Optimization

2020 ◽  
Vol 12 (20) ◽  
pp. 3302
Author(s):  
Lei Yu ◽  
Chunsheng Li ◽  
Jie Chen ◽  
Pengbo Wang ◽  
Zhirong Men
Keyword(s):  
Particle Swarm Optimization ◽  
High Resolution ◽  
Global Convergence ◽  
Particle Swarm ◽  
Sar Images ◽  
Processing Efficiency ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Imaging Results ◽  
Phase Signal

To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO.

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