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.

A NewFault Detection and Diagnosis Method Based on Wigner-Ville Spectrum Entropy for the Rolling Bearing

2012 ◽  
Vol 197 ◽  
pp. 346-350 ◽  
Author(s):  
Ping Xie ◽  
Yu Xin Yang ◽  
Guo Qian Jiang ◽  
Yi Hao Du ◽  
Xiao Li Li
Keyword(s):  
Fault Detection ◽  
Rolling Bearing ◽  
Work Conditions ◽  
Fault Detection And Diagnosis ◽  
Vibration Signals ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Detection And Diagnosis ◽  
Critical Components

The rolling bearings are one of the most critical components in rotary machinery. To prevent unexpected bearing failure, it is crucial to develop the effective fault detection and diagnosis techniques to realize equipment’s near-zero downtime and maximum productivity. In this paper, a new fault detection and diagnosis method based on Wigner-Ville spectrum entropy (WVSE) is proposed. First, the local mean decomposition (LMD) and the Wigner-Ville distribution (WVD) are combined to develop a new feature extraction approach to extract the fault features in time-frequency domain of the bearing vibration signals. Second, the concept of the Shannon entropy is integrated into the WVD to define the Wigner-Ville spectrum entropy to quantify the energy variation in time-frequency distribution under different work conditions. The research results from the bearing vibration signals demonstrate that the proposed method based on WVSE can identify different fault patterns more accurately and effectively comparing with other methods based on singular spectrum entropy (SSE) or power spectrum entropy (PSE).

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 An Intelligent Bearing Fault Diagnosis Method Based on SF-SVM

2021 ◽  
Vol 1210 (1) ◽  
pp. 012004
Author(s):  
Bao’an Qiu ◽  
Pan Sun ◽  
Lili Li
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Support Vector ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Frequency Map ◽  
The Stability ◽  
Vibration Time ◽  
Short Time

Abstract Rolling bearing, as a key component of rotating machinery, its health status directly determines the stability and reliability of the whole machine. The research on its intelligent diagnosis method has important engineering value and academic significance. However, due to actual engineering conditions, the types of bearing failures and the amount of data are limited. Aiming at the difficulty of extracting and selecting bearing vibration features under limited sample constraints, this pa-per proposes an intelligent fault diagnosis method of SF-SVM. On the basis of the short-time Fourier change, the L2 regularized sparse filter is used to extract the unsupervised feature of the bearing vibration time-frequency map. After obtaining the typical features of the bearing, the support vector machine is used for diagnosis.

scholarly journals Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals

2021 ◽  
Author(s):  
Yong Chang ◽  
Guangqing Bao ◽  
Sikai Cheng ◽  
Ting He ◽  
Qiaoling Yang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
Bearing Vibration

scholarly journals Time-Shift Multiscale Fuzzy Entropy and Laplacian Support Vector Machine Based Rolling Bearing Fault Diagnosis

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 602 ◽  
Author(s):  
Xiaolong Zhu ◽  
Jinde Zheng ◽  
Haiyang Pan ◽  
Jiahan Bao ◽  
Yifang Zhang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Time Shift ◽  
Support Vector ◽  
Vibration Signals ◽  
Diagnosis Method

Multiscale entropy (MSE), as a complexity measurement method of time series, has been widely used to extract the fault information hidden in machinery vibration signals. However, the insufficient coarse graining in MSE will result in fault pattern information missing and the sample entropy used in MSE at larger factors will fluctuate heavily. Combining fractal theory and fuzzy entropy, the time shift multiscale fuzzy entropy (TSMFE) is put forward and applied to the complexity analysis of time series for enhancing the performance of MSE. Then TSMFE is used to extract the nonlinear fault features from vibration signals of rolling bearing. By combining TSMFE with the Laplacian support vector machine (LapSVM), which only needs very few marked samples for classification training, a new intelligent fault diagnosis method for rolling bearing is proposed. Also the proposed method is applied to the experiment data analysis of rolling bearing by comparing with the existing methods and the analysis results show that the proposed fault diagnosis method can effectively identify different states of rolling bearing and get the highest recognition rate among the existing methods.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

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