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

By Jinhao Lei, Chao Liu, Dongxiang Jiang. Renewable Energy, 2019

442 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/214b5f19ee27236ecac8e6154a8e50ba619a4e27

Citations in the corpus

• A review on the long short-term memory model (2020, unknown): “This is the case of the fault diagnosis in Lei et al. (2019) where the authors used an LSTM-based framework for condition monitoring of wind turbines using only raw time series data gathered by a single or multiple sensors.”