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ELM Based Representational Learning for Fault Diagnosis of Wind Turbine Equipment
Zhixin Yang; Xianbo Wang; Pak Kin Wong; Jianhua Zhong
2015
Conference NameThe International Conference on Extreme Learning Machines (ELM2015)
Source PublicationProceedings of ELM-2015
Volume2
Pages169-178
Conference DateDec 2015
Conference PlaceHangzhou, China
Abstract

The data preprocessing, feature extraction, classifier training and testing play as the key components in a typical fault diagnosis system. This paper proposes a new application of extreme learning machines (ELM) in an integrated manner, where multiple ELM layers play correspondingly different roles in the fault diagnosis framework. The ELM based representational learning framework integrates functions including data preprocessing, feature extraction and dimension reduction. In the novel framework, an ELM based autoencoder is trained to get a hidden layer output weight matrix, which is then used to transform the input data into a new feature representation. Finally, a single layered ELM is applied for fault classification. Compared with existing feature extraction methods, the output weight matrix is treated as the mapping result with weighted distribution of input vector. It avoids wiping off “insignificant” feature information that may convey some undiscovered knowledge. The proposed representational learning framework does not need parameters fine-tuning with iterations. Therefore, the training speed is much faster than the traditional back propagation-based DL or support vector machine method. The experimental tests are carried out on a wind turbine generator simulator, which demonstrates the advantages of this method in both speed and accuracy.

KeywordFault Diagnosis Autoencoder Wind Turbine Representational Learning Classification Extreme Learning Machines
DOIhttps://www.doi.org/10.1007/978-3-319-28373-9_14
URLView the original
Language英语
Fulltext Access
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Document TypeConference paper
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationDepartment of Electromechanical EngineeringFaculty of Science and Technology, University of MacauMacau SARChina
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhixin Yang,Xianbo Wang,Pak Kin Wong,et al. ELM Based Representational Learning for Fault Diagnosis of Wind Turbine Equipment[C],2015:169-178.
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