Amazing technological breakthrough possible @S-Logix pro@slogix.in

Office Address

  • #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
  • pro@slogix.in
  • +91- 81240 01111

Social List

Fault diagnosis of wind turbine based on Long Short-term memory networks - 2019

Fault Diagnosis Of Wind Turbine Based On Long Short-Term Memory Networks

Research Area:  Machine Learning

Abstract:

Time-series data is widely adopted in condition monitoring and fault diagnosis of wind turbines as well as other energy systems, where long-term dependency is essential to form the classifiable features. To address the issues that the traditional approaches either rely on expert knowledge and handcrafted features or do not fully model long-term dependencies hidden in time-domain signals, this work presents a novel fault diagnosis framework based on an end-to-end Long Short-term Memory (LSTM) model, to learn features directly from multivariate time-series data and capture long-term dependencies through recurrent behaviour and gates mechanism of LSTM. Experimental results on two wind turbine datasets show that our method is able to do fault classification effectively from raw time-series signals collected by single or multiple sensors and outperforms state-of-art approaches. Furthermore, the robustness of the proposed framework is validated through the experiments on small dataset with limited data.

Keywords:  

Author(s) Name:  Jinhao Lei,Chao Liu,Dongxiang Jiang

Journal name:  Renewable Energy

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.renene.2018.10.031

Volume Information:  Volume 133, April 2019, Pages 422-432