Fault diagnosis of railway roller bearing based on vibration analysis and information fusion

2012 ◽  
Vol 131 (4) ◽  
pp. 3307-3307 ◽  
Author(s):  
Bin Chen ◽  
Zhaoli Yan ◽  
Xiaobin Cheng ◽  
Wei Liu
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Vibration Analysis ◽  
Roller Bearing

Recent Progress of Vibration Analysis on Fault Diagnosis for Rolling Bearing

2011 ◽  
Vol 84-85 ◽  
pp. 544-547
Author(s):  
En Gao Peng ◽  
Zheng Lin Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Analysis ◽  
Recent Progress ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Development Trend ◽  
Existing Problems ◽  
Detection Approach ◽  
Diagnosis Method ◽  
The Development Trend

Rolling bearing is extensively used in various areas including shipbuilding, aircraft, mining, manufacturing, agriculture, etc. The breakdowns of the bearings may do unexpected hazardous to the machinery. Therefore, it is crucial for engineers and researchers to monitor the bearing conditions in time in order to prevent the malfunctions of the plants. Hence the vibration analysis accompanying other condition monitoring methodologies has been successfully used in the field of roller bearings fault diagnosis as well as other key components. The fault diagnosis method of roller bearing using vibration analysis was introduced in this paper. And the development trend of vibration analysis was presented. Meanwhile the main existing problems in fault diagnosis of roller bearing were discussed. It concludes that the integration of vibration analysis and other detection approach can provide effective and reliable fault diagnosis results.

Fault Detection for Roller Bearing with Vibration Analysis and Information Fusion in Sorting Machine Induction

2014 ◽  
Vol 494-495 ◽  
pp. 805-808
Author(s):  
Wei Chen ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Si Cheng Nian
Keyword(s):  
Information Fusion ◽  
Vibration Analysis ◽  
High Speed ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Independent Evidence ◽  
Sorting Machine ◽  
Metal Metal ◽  
Output Information

Due to continuous metal-metal contacts in heavy and high-speed operating conditions, sorting machine inductions roller bearing easily occurs malfunctions. Therefore, its crucial to make incipient fault diagnosis. This paper presents a novel diagnosis algorithm using vibration analysis and information fusion. In the algorithm, vibration signal for roller bearing is firstly analyzed. Then extracted fault features are used as input eigenvector of constructed neural network classifier. In order to improve diagnosis accuracy, take output information of each single classifier as independent evidence, and aggregate them using improved Dempsters combination rule. Experiment results show that proposed algorithm has high accuracy of 99.5%.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information

Power Transformer Fault Diagnosis Based on Vibration Correlation Analysis

2014 ◽  
Author(s):  
Kaixing Hong ◽  
Hai Huang
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Fundamental Frequency ◽  
Vibration Analysis ◽  
Laboratory Tests ◽  
Power Transformer ◽  
Assessment Model ◽  
Vibration Method ◽  
Proposed Model ◽  
Transformer Fault

In this paper, a condition assessment model using vibration method is presented to diagnose winding structure conditions. The principle of the model is based on the vibration correlation. In the model, the fundamental frequency vibration analysis is used to separate the winding vibration from the tank vibration. Then, a health parameter is proposed through the vibration correlation analysis. During the laboratory tests, the model is validated on a test transformer, and manmade deformations are provoked in a special winding to compare the vibrations under different conditions. The results show that the proposed model has the ability to assess winding conditions.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

scholarly journals Mine ventilator fault diagnosis based on information fusion technique

2009 ◽  
Vol 1 (1) ◽  
pp. 1484-1488 ◽  
Author(s):  
Shi Li-ping ◽  
Han Li ◽  
Wang Ke-wu ◽  
Zhang Chuan-juan
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Fusion Technique ◽  
Information Fusion Technique
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