Next Article in Journal
Development and a Validation of a Charge Sensitive Organic Rankine Cycle (ORC) Simulation Tool
Next Article in Special Issue
Application of a Diffuser Structure to Vertical-Axis Wind Turbines
Previous Article in Journal
A Wavelet-Based Unified Power Quality Conditioner to Eliminate Wind Turbine Non-Ideality Consequences on Grid-Connected Photovoltaic Systems
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Energies 2016, 9(6), 379;

Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau SAR 999078, China
This paper is an extended version of our paper published in ELM Based Representational Learning for Fault Diagnosis of Wind Turbine Equipment. In Proceedings of the Extreme Learning Machines 2015, Hangzhou, China, 15–17 December 2015.
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Lance Manuel
Received: 18 March 2016 / Revised: 12 May 2016 / Accepted: 13 May 2016 / Published: 24 May 2016
(This article belongs to the Special Issue Modeling and Simulation for Wind Turbine Loads Analysis)
Full-Text   |   PDF [3484 KB, uploaded 24 May 2016]   |  


Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines. View Full-Text
Keywords: fault diagnosis; wind turbine; classification; extreme learning machines (ELM); autoencoder (AE) fault diagnosis; wind turbine; classification; extreme learning machines (ELM); autoencoder (AE)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Yang, Z.-X.; Wang, X.-B.; Zhong, J.-H. Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach. Energies 2016, 9, 379.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top