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

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

Damage Assessment of Structures VI - Key Engineering Materials ◽

10.4028/0-87849-976-8.373 ◽

2005 ◽

pp. 373-382

Author(s):

Qiao Hu ◽

Zhengjia He ◽

Yanyang Zi ◽

Zhou Suo Zhang ◽

Yaguo Lei

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Support Vector Machines ◽

Power Plant ◽

Empirical Mode Decomposition ◽

Support Vector ◽

Intelligent Fault Diagnosis ◽

Mode Decomposition ◽

Vector Machines

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Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2013.07.006 ◽

2013 ◽

Vol 41 (1-2) ◽

pp. 127-140 ◽

Cited By ~ 161

Author(s):

Xiaoyuan Zhang ◽

Jianzhong Zhou

Keyword(s):

Fault Diagnosis ◽

Support Vector Machines ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Support Vector ◽

Rolling Element Bearings ◽

Mode Decomposition ◽

Vector Machines ◽

Rolling Element

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

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2020.62.1.34 ◽

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%.

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