Sparse Time-Frequency Representation for Incipient Fault Diagnosis of Wind Turbine Drive Train

• Published in: IEEE transactions on instrumentation and measurement Vol. 67; no. 11; pp. 2616 - 2627
• Main Authors: Yang, Boyuan, Liu, Ruonan, Chen, Xuefeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Basis functions; Diagnostic systems; Dictionaries; Electric power generation; Fault detection; Fault diagnosis; Field tests; Frequency distribution; Frequency modulation; Harmonic analysis; Maintenance costs; Noise reduction; Powertrain; Representations; sparse representation; Time-frequency analysis; time-frequency representation; union of redundant dictionary (URD); Vibration; Vibrations; Waveforms; Wind power; wind turbine (WT); Wind turbines
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2018.2828739

As wind power attracts increasing attention and wind turbines (WTs) capacity expands, fault diagnosis of WT is playing a more and more important role in improving reliability, minimizing down time, reducing maintenance costs, and providing reliable power generation. In this paper, a novel sparse time-frequency representation (STFR) method is proposed to increase the diagnostic precision of incipient faults. The proposed method can be applied once the condition is detected as abnormal according to the VDI3834 vibration threshold standard in WT fault diagnosis systems. The proposed method is a novel signal representation method based on the sparse representation theory and Wigner-Ville distribution (WVD), which can overcome the limitations of traditional basis functions expansion and time-frequency analysis methods. In this method, a union of redundant dictionary (URD) is constructed on the basis of the underlying prior information of the oscillate characteristics with multicomponent coupling effect and different morphological waveforms. Therefore, the vibration signal can be sparsely represented over the URD. Then, the sparse coefficients and corresponding atoms can be obtained by solving the basis pursuit denoising problem via alternating direction method of multipliers. Based on the combination of the WVD of each atom and corresponding sparse coefficient, the time-frequency distribution of the vibration signal can be obtained. To verify the effectiveness of the STFR method, a simulation and two field tests in the wind farm are performed. The comparison results with state-of-the-art methods illustrate the superiority and robustness of the proposed method in the engineering applications.