Application Research of Feature Extraction Method of Power Equipment Nameplate

2019 ◽  
Vol 09 (11) ◽  
pp. 2084-2097
Author(s):  
彦直 吴
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Power Equipment ◽  
Application Research ◽  
Feature Extraction Method

Research on Vibration Signal Feature Extraction Method to the Wind Turbine Generator

2014 ◽  
Vol 902 ◽  
pp. 370-377
Author(s):  
Guo Xin Wu ◽  
Yun Bo Zuo ◽  
Yan Hui Shi
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Power ◽  
Extraction Method ◽  
Vibration Signal ◽  
Power Equipment ◽  
Feature Extraction Method ◽  
Current State ◽  
Diagnosis Method

Aiming at the safe operation of the wind turbine, a feature extraction method of vibration signal based on the principle of blind source separation was proposed. Blind source and the current state of fault signal was separated and predicated by Using historical data of wind turbine vibration signal as the observation noise, and then fault diagnosis signal mechanical operation was analyzed, the HMM/SVM series fault diagnosis models structure was proposed. By calculating the matching degree of unknown signal and wind power equipment in the state using HMM, the features for SVM was formed to achieve the finally discriminant. The experimental results showed that the fault diagnosis method can precisely and effectively complete the wind power equipment, higher than pure HMM or SVM diagnostic accuracy.

scholarly journals Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 685 ◽  
Author(s):  
Yongjie Zhai ◽  
Xu Yang ◽  
Yani Peng ◽  
Xinying Wang ◽  
Kang Bai
Keyword(s):  
Feature Extraction ◽  
Mutual Information ◽  
Extraction Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Power Equipment ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Normalized Mutual Information

The equipment condition monitoring based on computer hearing is a new pattern recognition approach, and the system formed by it has the advantages of noncontact and strong early warning abilities. Extracting effective features from the sound data of the running power equipment help to improve the equipment monitoring accuracy. However, the sound of running equipment often has the characteristics of serious noise, non-linearity and instationary, which makes it difficult to extract features. To solve this problem, a feature extraction method based on the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale improved permutation entropy (MIPE) is proposed. Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from the sound of running power equipment. The noise IMFs are then identified and eliminated through mutual information (MI) and mean mutual information (meanMI) of IMFs. Next, the normalized mutual information (norMI) and MIPE are calculated respectively, and norMI is utilized to weigh the corresponding MIPE result. Finally, based on the separability criterion, the weighted MIPE results are feature-dimensionally reduced to obtain the multiscale entropy feature of the sound. The experimental results show that the classification accuracies of the method under the conditions of no noise and 5 dB reach 96.7% and 89.9%, respectively. In practice, the proposed method has higher reliability and stability for the sound feature extraction of the running power equipment.

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

Sign in / Sign up

Export Citation Format

Share Document