Research on gearbox impact feature extraction method based on improved ESMD

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

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Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

10.20855/ijav.2019.24.21325 ◽

2019 ◽

Vol 24 (2) ◽

pp. 303-311 ◽

Cited By ~ 5

Author(s):

Xiaoxia Zheng ◽

Guowang Zhou ◽

Dongdong Li ◽

Haohan Ren

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Mode Decomposition ◽

Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

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An optimized variational mode decomposition method and its application in vibration signal analysis of bearings

Structural Health Monitoring ◽

10.1177/14759217211057444 ◽

2021 ◽

pp. 147592172110574

Author(s):

Jun Gu ◽

Yuxing Peng ◽

Hao Lu ◽

Xiangdong Chang ◽

Shuang Cao ◽

...

Keyword(s):

Mean Squared Error ◽

Input Parameter ◽

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Penalty Factor

The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.

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An approach for the impact feature extraction method based on improved modal decomposition and singular value analysis

Journal of Vibration and Control ◽

10.1177/1077546318811410 ◽

2018 ◽

Vol 25 (5) ◽

pp. 1096-1108 ◽

Cited By ~ 3

Author(s):

Fengfeng Bie ◽

Kirill V. Horoshenkov ◽

Jin Qian ◽

Junfeng Pei

Keyword(s):

Feature Extraction ◽

Singular Value ◽

Support Vector ◽

Driving Mechanism ◽

Machine Method ◽

Value Analysis ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Reciprocating Pump ◽

The Impact

For non-stationary vibration useful information of the impact feature tends to be overwhelmed with strong routine components, which make it difficult to implement pattern recognition. This paper proposes improved signal processing methods of variational mode decomposition (VMD) and singular value decomposition (SVD) for non-stationary impact feature extraction in application to condition monitoring of reciprocating machinery. The impact feature is firstly simulated with the dynamics' analysis of the driving mechanism of a reciprocating pump. Through comparison the merit of the improved VMD method is demonstrated. The singular value of the decomposed modes is extracted with the SVD method. The support vector machine method is used as the classifier for the extracted set of features. The performance of the proposed VMD-based method is validated practically through a set of measured data from the reciprocating pump setup.

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Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽

10.3390/e22040468 ◽

2020 ◽

Vol 22 (4) ◽

pp. 468 ◽

Cited By ~ 1

Author(s):

Dongri Xie ◽

Hamada Esmaiel ◽

Haixin Sun ◽

Jie Qi ◽

Zeyad A. H. Qasem

Keyword(s):

Feature Extraction ◽

Correlation Coefficient ◽

Extraction Method ◽

Recognition Rate ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Feature Extraction Method ◽

Radiated Noise ◽

Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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Inspection method of combine assembly quality based on optimized VMD

Journal of Physics Conference Series ◽

10.1088/1742-6596/2125/1/012021 ◽

2021 ◽

Vol 2125 (1) ◽

pp. 012021

Author(s):

Menghui Xuan ◽

Sixia Zhao ◽

Mengnan Liu ◽

Liyou Xu ◽

Xiaoliang Chen

Keyword(s):

Search Algorithm ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Support Vector ◽

Modal Parameter ◽

Assembly Quality ◽

Inspection Method ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Penalty Factor

Abstract Aiming at the problems of low assembly accuracy and difficult to detect assembly quality of combine, a method of combine assembly quality detection based on sparrow search algorithm (SSA) optimized variational mode decomposition (VMD) and particle swarm optimization (PSO) optimized least squares support vector machine (LSSVM) was proposed, Firstly, the sparrow search algorithm is used to obtain the optimal VMD decomposition modal parameter K and penalty factor α, then the combined vibration signal of combine harvester is decomposed into intrinsic modal components of different center frequencies by using the best parameter combination [K, α]. Finally, the feature vector is used as the input of LSSVM classifier to classify different fault features. The analysis results show that the classification accuracy of SSA-VMD joint feature extraction method is 99.5%, which is 17.5% and 9.5% higher than ensemble empirical mode decomposition (EEMD) and fixed parameter VMD, which verifies the superiority of this method in the detection of combine assembly quality.

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

Mathematical Problems in Engineering ◽

10.1155/2019/3089417 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 4

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.

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Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

Entropy ◽

10.3390/e23060762 ◽

2021 ◽

Vol 23 (6) ◽

pp. 762

Author(s):

Maoyou Ye ◽

Xiaoan Yan ◽

Minping Jia

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Classification Model ◽

Permutation Entropy ◽

Support Vector ◽

Detection Accuracy ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Bearing Vibration

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

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Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

Shock and Vibration ◽

10.1155/2009/519502 ◽

2009 ◽

Vol 16 (1) ◽

pp. 89-98 ◽

Cited By ~ 55

Author(s):

Junsheng Cheng ◽

Dejie Yu ◽

Jiashi Tang ◽

Yu Yang

Keyword(s):

Fault Diagnosis ◽

Feature Vector ◽

Roller Bearing ◽

Vibration Signal ◽

Singular Values ◽

Rotating Machinery ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

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Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Entropy ◽

10.3390/e22020209 ◽

2020 ◽

Vol 22 (2) ◽

pp. 209 ◽

Cited By ~ 4

Author(s):

Shaohua Xue ◽

Jianping Tan ◽

Lixiang Shi ◽

Jiwei Deng

Keyword(s):

Fault Diagnosis ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Support Vector ◽

Sensitivity Index ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition ◽

New Perspective

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

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Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Shock and Vibration ◽

10.1155/2016/3891429 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Liye Zhao ◽

Wei Yu ◽

Ruqiang Yan

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Correlation Coefficients ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

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