Forecasting Non-normal Demand by Support Vector Machines with Ensemble Empirical Mode Decomposition

2011 ◽  
Vol 3 (3) ◽  
pp. 81-91
Author(s):  
Yukun Bao ◽  
Rui Zhang ◽  
Tao Xiong ◽  
Zhongyi Hu
Keyword(s):  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Mode Decomposition ◽  
Vector Machines

scholarly journals Forecasting Air Passenger Traffic by Support Vector Machines with Ensemble Empirical Mode Decomposition and Slope-Based Method

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Yukun Bao ◽  
Tao Xiong ◽  
Zhongyi Hu
Keyword(s):  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
End Effect ◽  
Modeling Framework ◽  
Passenger Traffic ◽  
Straightforward Application ◽  
Mode Decomposition ◽  
Vector Machines

With regard to the nonlinearity and irregularity along with implicit seasonality and trend in the context of air passenger traffic forecasting, this study proposes an ensemble empirical mode decomposition (EEMD) based support vector machines (SVMs) modeling framework incorporating a slope-based method to restrain the end effect issue occurring during the shifting process of EEMD, which is abbreviated as EEMD-Slope-SVMs. Real monthly air passenger traffic series including six selected airlines in USA and UK were collected to test the effectiveness of the proposed approach. Empirical results demonstrate that the proposed decomposition and ensemble modeling framework outperform the selected counterparts such as single SVMs (straightforward application of SVMs), Holt-Winters, and ARIMA in terms of RMSE, MAPE, GMRAE, and DS. Additional evidence is also shown to highlight the improved performance while compared with EEMD-SVM model not restraining the end effect.

A novel bevel gear fault diagnosis method based on ensemble empirical mode decomposition and support vector machines

2020 ◽  
Vol 62 (1) ◽  
pp. 34-41
Author(s):  
Sun Yanqiang ◽  
Chen Hongfang ◽  
Shi Zhaoyao ◽  
Tang Liang
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Bevel Gear ◽  
Support Vector ◽  
Analysis Method ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Vector Machines

A novel analysis method is proposed based on ensemble empirical mode decomposition (EEMD) and support vector machines (SVMs) for the fault diagnosis of bevel gears. Firstly, the EEMD method is used to decompose the fluctuations in the original gear noise signals into different timescales so as to obtain several intrinsic mode functions (IMFs). The meshing frequency components in the decomposition results are reconstructed to eliminate the influence of interference noise. Then, time-synchronous averaging (TSA) is applied in further denoising to weaken signals independent of the gear meshing frequency. After denoising, various signal characteristics are calculated. Obvious signal characteristics for different fault states are selected as a set of feature vectors. Finally, a particle optimisation method is used to optimise SVM parameters and the feature vectors are input as training samples into an SVM in order to achieve fault recognition. The experimental results show that this novel analysis method can effectively diagnose different conditions of the bevel gear and achieve an identification rate for gear faults of 98.33%.

Intelligent Fault Diagnosis in Power Plant Using Empirical Mode Decomposition, Fuzzy Feature Extraction and Support Vector Machines

2005 ◽  
Vol 293-294 ◽  
pp. 373-382 ◽  
Author(s):  
Qiao Hu ◽  
Zheng Jia He ◽  
Yanyang Zi ◽  
Zhou Suo Zhang ◽  
Yaguo Lei
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Turbo Generator ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Multi Classification

In this paper, a novel intelligent fault diagnosis method based on empirical mode decomposition (EMD), fuzzy feature extraction and support vector machines (SVM) is proposed. The method consists of two stages. In the first stage, intrinsic mode components are obtained with EMD from original signals and converted into fuzzy feature vectors, and then the mechanical fault can be detected. In the second stage, these extracted fuzzy feature vectors are input into the multi-classification SVM to identify the different abnormal cases. The proposed method is applied to the classification of a turbo-generator set under three different operating conditions. Testing results show that the classification accuracy of the proposed model is greatly improved compared with the multi-classification SVM without feature extraction and the multi-classification SVM with extracting the fuzzy feature from wavelet packets, and the faults of steam turbo-generator set can be correctly and rapidly diagnosed using this model.

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