scholarly journals Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Entropy ◽  
2017 ◽  
Vol 19 (8) ◽  
pp. 421 ◽  
Author(s):  
Qing Li ◽  
Steven Liang
Keyword(s):  
Fault Diagnosis ◽  
Matching Pursuit ◽  
Vibration Signal ◽  
L1 Norm ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Incipient Fault ◽  
Nonconvex Penalty ◽  
Treat All ◽  
Incipient Fault Diagnosis

The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.

scholarly journals Correction: Li, Q.; Liang, S.Y. Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique. Entropy 2017, 19, 421

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 483
Author(s):  
Qing Li ◽  
Steven Y. Liang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Incipient Fault ◽  
Nonconvex Penalty ◽  
Incipient Fault Diagnosis ◽  
Made In

The authors were not aware of some errors and imprecise descriptions made in the proofreading phase, therefore, we wish to make the following corrections to this paper [...]

scholarly journals A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 995 ◽  
Author(s):  
Tao Liang ◽  
Hao Lu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Mode Decomposition

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

Information Fusion of the Vibration and Acoustic Signals Based Rolling Bearing Incipient Fault Diagnosis Method

2013 ◽  
Vol 774-776 ◽  
pp. 1499-1502
Author(s):  
Ting Feng Ming ◽  
Yong Xiang Zhang ◽  
Jing Li
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Information Fusion ◽  
Vibration Signal ◽  
Acoustic Resonance ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Fusion Analysis

The feature of correlation analysis were described and applied to analyzing the vibration signal of the gearbox. Aiming to that the diagnosis effect of the rolling bearings incipient fault was not good through the vibration spectrum and the resonance demodulation spectrum directly, the information fusion technology based on the correlation analysis is proposed to processing the vibration and acoustic resonance demodulation signal. The experimental results show that the presented correlation fusion analysis technology can be as the basis of the effective fault diagnosis method for the rolling bearings incipient defect.

scholarly journals Fault Diagnosis for Rolling Bearings Using Optimized Variational Mode Decomposition and Resonance Demodulation

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 739
Author(s):  
Chunguang Zhang ◽  
Yao Wang ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Signal Features ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Two Parameters ◽  
Envelope Spectrum

It is difficult to extract the fault signal features of locomotive rolling bearings and the accuracy of fault diagnosis is low. In this paper, a novel fault diagnosis method based on the optimized variational mode decomposition (VMD) and resonance demodulation technology, namely GNVRFD, is proposed to realize the fault diagnosis of locomotive rolling bearings. In the proposed GNVRFD method, the genetic algorithm and nonlinear programming are combined to design a novel parameter optimization algorithm to adaptively optimize the two parameters of the VMD. Then the optimized VMD is employed to decompose the collected vibration signal into a series of intrinsic mode functions (IMFs), and the kurtosis value of each IMF is calculated, respectively. According to the principle of maximum value, two most sensitive IMF components are selected to reconstruct the vibration signal. The resonance demodulation technology is used to decompose the reconstructed vibration signal in order to obtain the envelope spectrum, and the fault frequency of locomotive rolling bearings is effectively obtained. Finally, the actual data of rolling bearings is selected to testify the effectiveness of the proposed GNVRFD method. The experiment results demonstrate that the proposed GNVRFD method can more accurately and effectively diagnose the fault of locomotive rolling bearings by comparing with other fault diagnosis methods.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang-qiang Chen ◽  
Shao-wu Dai ◽  
Hong-de Dai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Main Fault

The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

Incipient Fault Diagnosis of the Planetary Gearbox Based on Improved Variational Mode Decomposition and Frequency-Weighted Energy Operator

2019 ◽  
Author(s):  
Hongkun Li ◽  
Chaoge Wang ◽  
Jiayu Ou
Keyword(s):  
Fault Diagnosis ◽  
Energy Operator ◽  
Energy Difference ◽  
Variational Mode Decomposition ◽  
Decomposition Level ◽  
Planetary Gearbox ◽  
Weighted Energy ◽  
Mode Decomposition ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

Abstract Planetary gearbox is widely used in large and complex mechanical equipment such as wind power generation, helicopters and petrochemical industry. Gear failures occur frequently in working conditions at low speeds, high service load and harsh operating environments. Incipient fault diagnosis can avoid the occurrence of major accidents and loss of personnel property. Aiming at the problems that the incipient fault of planetary gearbox is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, a improved VMD algorithm based on energy difference as an evaluation parameter to automatically determine the decomposition level k is proposed. On this basis, a new method for early fault feature extraction of planetary gearbox based on the improved VMD and frequency-weighted energy operator is proposed. Firstly, the vibration signal is pre-decomposed by VMD, and the energy difference between the component signal and the original signal under different K-values is calculated respectively. The optimal decomposition level k is determined according to the energy difference curve. Then, according to kurtosis criterion, sensitive components are selected from the k modal components obtained by the decomposition to reconstruct. Finally, a new frequency-weighted energy operator is used to demodulate the reconstructed signal. The fault characteristic frequency information of the planetary gearbox can be accurately extracted from the energy spectrum. The method is applied to the simulation fault data and actual data of planetary gearbox, and the weak fault characteristics of planetary gearbox are extracted effectively, and the early fault characteristics are distinguished. The results show that the new method has certain application value and practical significance.

Sign in / Sign up

Export Citation Format

Share Document