scholarly journals An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2235 ◽  
Author(s):  
Zhiqiang Liao ◽  
Liuyang Song ◽  
Peng Chen ◽  
Zhaoyi Guan ◽  
Ziye Fang ◽  
...  
Keyword(s):  
Nearest Neighbors ◽  
Singular Value ◽  
Statistical Information ◽  
Information Criteria ◽  
Signal Filtering ◽  
Bearing Fault ◽  
Diagnosis Method ◽  
False Nearest Neighbors

A Bearing Fault Diagnosis Method Using Fusion of Lie Group Classifier and GA

2013 ◽  
Vol 790 ◽  
pp. 659-662
Author(s):  
Si Yuan Zhao ◽  
Wang Tao ◽  
Ge Xin ◽  
Yun Liu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Lie Group ◽  
Rolling Bearing ◽  
Singular Value ◽  
Support Vector ◽  
Ring Fault ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

A novel bearing fault diagnosis method based on Lie group was proposed, and genetic algorithm (GA) was introduced to optimize feature amount. This method was applied to inner ring fault, outer ring fault and rolling element fault of rolling bearing. Firstly, the rolling bearing vibration signal was decomposed as intrinsic model functions (IMF) by using the empirical mode decomposition (EMD) method. The energy of every IMF and the singular value of the IMF matrix were chosen as features. The Shannon and Renyi entropy of the energy and singular value distribution were also extracted. Secondly genetic algorithm was used to reduce feature redundancy, with lowest classifier error rate and least feature amount as finess function. At last, a comparison was made between this method and least square support vector machine (LSSVM).The results showed that Lie group clkassifier was more sensitivce to feature. This method could use less feature amount to diagnose fault.

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 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
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