Researchers from Beihang University Report Details of New Studies and Findings in the Area of Engineering (Fault Diagnosis of Wind Turbine Gearbox Based On Deep Bi-directional Long Short-term Memory Under Time-varying Non-stationary Operating ...)
- Feb 26, 2020 12:56 am GMT
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2020 FEB 25 (NewsRx) -- By a News Reporter-Staff News Editor at Engineering Daily News -- Current study results on Engineering have been published. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Fault diagnosis of wind turbine (WT) gearboxes can reduce unexpected downtime and maintenance costs. In this paper, a new fault diagnosis framework is proposed based on deep bi-directional Long Short-term Memory (DB-LSTM).”
Funders for this research include National Natural Science Foundation of China, Program for Changjiang Scholars and Innovative Research Team in University.
Our news editors obtained a quote from the research from Beihang University, “Even though deep learning has been used in fault diagnosis of rotating machines, deep learning diagnosis models with the input of raw time-series or frequency data face computational challenges. Additionally, the deviation between datasets can be triggered easily by operating condition variation, which will highly reduce the performance of fault diagnosis models. However, in most studies, several constant operating conditions (e.g., selected some rotational speeds and loads) are used in the experiments, which may not reflect time-varying non-stationary operating conditions of WT gearbox and cannot be applicable in real-life applications. In this work, the experiments are designed that the real rotor speed of WT spindle input to the WT drivetrain test rig to simulate the actual time-varying non-stationary operating conditions. Ten common time-domain features are all fed into the DB-LSTM network to construct fault diagnosis model, which eliminates the need for selecting suitable features manually and improves training time. Vibration data collected by three accelerometers are used to validate the effectiveness and feasibility of the proposed method.”
According to the news editors, the research concluded: “The proposed method is also compared with four existing diagnosis models, and the results are discussed.”
For more information on this research see: Fault Diagnosis of Wind Turbine Gearbox Based On Deep Bi-directional Long Short-term Memory Under Time-varying Non-stationary Operating Conditions. IEEE Access, 2019;7():155219-155228. IEEE Access can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.
The news editors report that additional information may be obtained by contacting Z. Qian, Beihang University, School of Instrumentation & Optoelectronics Engineering, Beijing 100083, People’s Republic of China. Additional authors for this research include L.X. Cao, Z.K. Huang, H. Zareipour and F.H. Zhang.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/ACCESS.2019.2947501. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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