Fault diagnosis of wind turbine gearbox based on the optimized LSTM neural network with cosine loss

Aijun Yin, Yinghua Yan, Zhiyu Zhang, Chuan Li, René Vinicio Sánchez

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis.

Original languageEnglish
Article number2339
JournalSensors (Switzerland)
Issue number8
StatePublished - Apr 2020

Bibliographical note

Funding Information:
Funding: This work was supported by the Key Science and Technology Research Project of Chongqing under grant cstc2018jszx-cyztzxX0032.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.


  • Cosine loss
  • Gearbox fault
  • Long short-term memory network
  • Wind turbine


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