Da Wang
5 Papers
17 Citations
Da Wang is an academic researcher. The author has contributed to research in topics: Feature selection & Random forest. The author has an hindex of 3, co-authored 4 publications.
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Papers
Power Quality Disturbance Feature Selection and Pattern Recognition Based on Image Enhancement Techniques
TL;DR: The results of the simulation and contrast experiments show that the new method can determine the optimal classification subset, which recognizes the PQD signals effectively in different noise environments, and has higher signal processing efficiency compared with the EMD and ST methods.
Power quality disturbances classification using rotation forest and multi-resolution fast S-transform with data compression in time domain
TL;DR: The simulation results show that the new method can effectively compress the time–frequency matrix of the existing S-transform (ST) method; the space complexity of the ST modular matrix is reduced significantly and has higher accuracy.
39
Acoustic feature enhancement in rolling bearing fault diagnosis using sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted method
Yaochun Hou,Changqing Zhou,Changming Tian,Da Wang,Weiting He,Wenjun Huang,Peng-hui Wu,Dazhuan Wu +7 more
TL;DR: In this paper , a sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method is proposed for bearing fault feature enhancement and diagnosis based on acoustic signals.
22
Patent
Wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives
Huang Nantian,Wang Bin,Da Wang,Cai Guowei,Yang Dongfeng,Huang Dawei,Fang Lihua,Yang Xuehang,Liu Bo,Zhang Liang,Kong Lingguo,Wang Yantao +11 more
- 19 Jun 2018
TL;DR: In this article, a wind turbine generator set bearing mechanical fault diagnosis method considering multi-class objectives is presented, which is characterized by comprising the steps of wind turbine generators set bearing vibration signal acquisition and vibration signal processing.
17
Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample.
TL;DR: A novel bearing fault diagnosis method based on the one-class classification concept and random forest is proposed for reducing the impact of the limitations of the fault training sample and can significantly improve the classification accuracy compared with traditional methods in different diagnostic target.