scholarly journals A New Method of Fault Feature Extraction Based on Hierarchical Dispersion Entropy

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Chen ◽  
Xiaoqiang Zhao ◽  
HongMei Jiang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
White Noise ◽  
Gaussian White Noise ◽  
Rolling Bearing ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Feature Information ◽  
Fault Feature Extraction

In the process of fault feature extraction of rolling bearing, the feature information is difficult to be extracted fully. A novel method of fault feature extraction called hierarchical dispersion entropy is proposed in this paper. In this method, the vibration signals firstly are decomposed hierarchically. Secondly, dispersion entropies of different nodes are calculated. Hierarchical dispersion entropy can realize the comprehensive feature extraction of the high- and low-frequency band information of vibration signals and overcome the problems that dispersion entropy and multiscale dispersion entropy are insufficient to extract the fault feature information of vibration signals. The feasibility of hierarchical dispersion entropy is obtained by analyzing the hierarchical dispersion entropy of Gaussian white noise and compared with the multiscale dispersion entropy of Gaussian white noise. Meanwhile, a fault diagnosis method for rolling bearings based on hierarchical dispersion entropy and k-nearest neighbor (KNN) classifier is developed. Finally, the superiority of the proposed fault diagnosis method is verified in the realization of fault diagnosis of the rolling bearing in different positions and different degrees of damage.

scholarly journals A Hybrid Gearbox Fault Diagnosis Method Based on GWO-VMD and DE-KELM

2021 ◽  
Vol 11 (11) ◽  
pp. 4996
Author(s):  
Gang Yao ◽  
Yunce Wang ◽  
Mohamed Benbouzid ◽  
Mourad Ait-Ahmed
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Optimization Techniques ◽  
Fault Classification ◽  
Vibration Signals ◽  
Kernel Parameter ◽  
Diagnosis Method ◽  
The Impact ◽  
Fault Feature Extraction

In this paper, a vibration signal-based hybrid diagnostic method, including vibration signal adaptive decomposition, vibration signal reconstruction, fault feature extraction, and gearbox fault classification, is proposed to realize fault diagnosis of general gearboxes. The main contribution of the proposed method is the combining of signal processing, machine learning, and optimization techniques to effectively eliminate noise contained in vibration signals and to achieve high diagnostic accuracy. Firstly, in the study of vibration signal preprocessing and fault feature extraction, to reduce the impact of noise and mode mixing problems on the accuracy of fault classification, Variational Mode Decomposition (VMD) was adopted to realize adaptive signal decomposition and Wolf Grey Optimizer (GWO) was applied to optimize parameters of VMD. The correlation coefficient was subsequently used to select highly correlated Intrinsic Mode Functions (IMFs) to reconstruct the vibration signals. With these re-constructed signals, fault features were extracted by calculating their time domain parameters, energies, and permutation entropies. Secondly, in the study of fault classification, Kernel Extreme Learning Machine (KELM) was adopted and Differential Evolutionary (DE) was applied to search its regularization coefficient and kernel parameter to further improve classification accuracy. Finally, gearbox vibration signals in healthy and faulty conditions were obtained and contrast experiences were conducted to validate the effectiveness of the proposed hybrid fault diagnosis method.

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

2021 ◽  
pp. 095440622110329
Author(s):  
Jianqun Zhang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Training Data ◽  
Variable Load ◽  
K Nearest Neighbor ◽  
Target Domain ◽  
Bearing Faults ◽  
Load Conditions

To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

A Rolling Bear Fault Diagnosis Method Based on Improved LMD and Order Analysis

2014 ◽  
Vol 530-531 ◽  
pp. 256-260
Author(s):  
Hui Juan Yuan ◽  
Jia Qi ◽  
Hong Mei Li ◽  
Jun Zhong Li ◽  
Xue Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Frequency Modulation ◽  
Rolling Bearing ◽  
Analysis Method ◽  
Vibration Signals ◽  
Order Analysis ◽  
Diagnosis Method ◽  
Bearing Vibration

This document explains and demonstrates how to predict the fault point of rolling bear. Rolling bearing vibration signals are decomposed by the LMD method to get several single components including amplitude modulation and frequency modulation signals. Combing the order analysis method can get the fault point of rolling bear.

Fault Diagnosis Method for Rectifier Circuit Based on Self-Organizing Map

2013 ◽  
Vol 321-324 ◽  
pp. 1930-1933 ◽  
Author(s):  
Run Xia Shen ◽  
Yi Min Lu ◽  
Qian Qian Liang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Self Organizing Map ◽  
Rectifier Circuit ◽  
Three Phase ◽  
Bridge Rectifier ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Relevant Experiment ◽  
Self Organizing

Fault feature extraction and recognition play crucial role in fault diagnosis. In this paper, a fault diagnosis method for three-phase fully-controlled bridge rectifier circuit based on Self-Organizing Map network is proposed. The method utilized the three phase AC input current as fault detection data. Then, perform spectrum analysis with the FFT, the fault data is trained through a Self-Organizing Map network for diagnosis. Simulation and relevant experiment verifying the proposed algorithm can classify various types of power electronics device faults accurately and rapidly.

scholarly journals Application of an Improved Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Gearbox Composite Fault Diagnosis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2861 ◽  
Author(s):  
Wenan Cai ◽  
Zhijian Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Original Signal ◽  
Minimum Entropy ◽  
Product Function ◽  
Diagnosis Method ◽  
Local Mean ◽  
Fault Feature Extraction ◽  
Coefficient Method

The fault feature extraction of gearbox is difficult to achieve under complex working conditions, and this paper presents a hybrid fault diagnosis method for gearbox based on the combining product function (CPF) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) methods. First, ensemble local mean decomposition (ELMD) is utilized to reduce the noise in original signal, and get a series of product functions (PFs), through the correlation coefficient method to remove false components and residual components. Then, multi-point kurtosis of the definition is achieved by calculating the multi-point kurtosis spectrum of each layer PF, and the fault feature period is extracted and the PFs without periodic impact are removed. After that, in order to maintain the integrity of the original signal, the PFs with the same period are recombined by the combined product function method. Finally, the different cycle interval is configured, reduce the noise through MOMEDA on the combined signal, to further extract the fault feature. The method is applied to the feature extraction of gear box composite fault to verify the feasibility of this method.

scholarly journals Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2524
Author(s):  
Huibin Zhu ◽  
Zhangming He ◽  
Juhui Wei ◽  
Jiongqi Wang ◽  
Haiyin Zhou
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Feature Fusion ◽  
Singular Value ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Frequency Feature

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 98
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen ◽  
Yueying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.

A Combination of WKNN to Fault Diagnosis of Rolling Element Bearings

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Yaguo Lei ◽  
Zhengjia He ◽  
Yanyang Zi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Rolling Element Bearings ◽  
K Nearest Neighbor ◽  
Vibration Signals ◽  
Multiple Classifiers ◽  
Operation Conditions ◽  
Rolling Element

This paper presents a new method for fault diagnosis of rolling element bearings, which is developed based on a combination of weighted K nearest neighbor (WKNN) classifiers. This method uses wavelet packet transform based on the lifting scheme to preprocess the vibration signals before feature extraction. Time- and frequency-domain features are all extracted to represent the operation conditions of the bearings totally. Sensitive features are selected after feature extraction. And then, multiple classifiers based on WKNN are combined to overcome the two disadvantages of KNN and therefore it may enhance the classification accuracy. The experimental results of the proposed method to fault diagnosis of the rolling element bearings show that this method enables the detection of abnormalities in bearings and at the same time identification of fault categories and levels.

scholarly journals The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiwen Qin ◽  
Dingxin Xu ◽  
Xiaogang Dong ◽  
Xueteng Cui ◽  
Siqi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Computer Hardware ◽  
Great Effort ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Deep Forest ◽  
Diagnosis Method ◽  
Feature Information ◽  
Types Of Faults

Rolling bearing fault diagnosis is a meaningful and challenging task. Most methods first extract statistical features and then carry out fault diagnosis. At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer equipment and great effort in hyperparameter tuning. To address these issues, a rolling bearing fault diagnosis method based on the improved deep forest algorithm is proposed. Firstly, the fault feature information of rolling bearing is extracted through multigrained scanning, and then the fault diagnosis is carried out by cascade forest. Considering the fitting quality and diversity of the classifier, the classifier and the cascade strategy are updated. In order to verify the effectiveness of the proposed method, a comparison is made with the traditional machine learning method. The results suggest that the proposed method can identify different types of faults more accurately and robustly. At the same time, it has very few hyperparameters and very low requirements on computer hardware.

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