Fault Diagnosis of Bearing Based on Empirical Mode Decomposition and Decision Directed Acyclic Graph Support Vector Machine

2009 ◽  
Author(s):  
Qiumian Hao ◽  
Zi-ying Wang ◽  
Mian-hao Qiu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Directed Acyclic Graph ◽  
Support Vector ◽  
Acyclic Graph ◽  
Mode Decomposition

Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2230-2242
Author(s):  
Ying Shi ◽  
Cai Yi ◽  
Jianhui Lin ◽  
Zhe Zhuang ◽  
Senhua Lai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Effective Measure ◽  
Vibration Data ◽  
Ranking Criteria ◽  
Mode Decomposition

In this article, a fault diagnosis approach for a pantograph is developed with collected vibration data from a test rig. Ensemble empirical mode decomposition is used to decompose the signals to get intrinsic mode function, and four kinds of entropies (permu1tation entropy, approximate entropy, sample entropy, and fuzzy entropy) reflecting the working state are extracted as the inputs of the support vector machine based on particle swarm optimization algorithm support vector machine. The effect of data length, embedded dimension, and other parameters on calculation of the entropy value has also been studied. Multiple feature ranking criteria are used to select the useful features and improve the fault diagnosis accuracy of certain measurement points. Experimental results on pantograph vibration analysis have then confirmed that the proposed method provides an effective measure for pantograph diagnosis.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

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