Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform. (June 2019)

• Record Type: Journal Article
• Authors: Teng, Wei; Ding, Xian; Cheng, Hao; Han, Chen; Liu, Yibing; Mu, Haihua
• Is Part Of: Renewable energy. Volume 136(2019)
• Page Start: 393
• Page End: 402
• Publication Date: 2019-06
• Subjects: Wind turbine gearbox -- Compound faults -- Modulation model -- Empirical wavelet transform; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.12.094
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Fault diagnosis of a wind turbine gearbox can schedule maintenance strategy on a wind turbine and save operational cost for wind farms. Owing to low rotational speed and weak vibration energy, fault diagnosis for planetary stage is arduous in a wind turbine gearbox involving multi-stage transmissions. A novel modulation model is presented to support the fault diagnosis of the planetary stage of a wind turbine gearbox, which is described as the mesh frequency of intermediate stage, high speed stage, or mechanical natural frequency of the gearbox is a carrier wave modulated by the mesh frequency of planetary stage with distributed faults. Even possessing this presented vibration model, the fault feature of planetary stage with lower rotational speed is easily concealed by the meshing vibration energy of ordinary stages with higher speed, especially when faults simultaneously arise in the ordinary stages. Aiming at the diagnosis of the above compound faults in an industrial wind turbine gearbox, empirical wavelet transform is utilized to adaptively find weak fault frequency in planetary stage as well as evident fault characteristics in other ordinary stages. Highlights: A modulation model with the mesh frequency of the planetary stage as modulating frequency is proposed. Empirical wavelet transform is applied to extract multiple fault features intelligently. The failure mechanism of the compound faults in a wind turbine gearbox is analyzed.