Wind Power Planetary Gearbox Fault Diagnosis Based on Optimized EFD Algorithm

被引：0
|
作者：
Wang G. [1 ]
Zhang X. [1 ]
Wang F. [1 ]
Hu M. [1 ]
机构：
[1] School of Mechanical Engineering, Tianjin University, Tianjin
来源：
Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology | 2023年 / 56卷 / 04期
基金：
中国国家自然科学基金;
关键词：
empirical Fourier decomposition; fault diagnosis; planetary gearbox; spectrum segmentation;
D O I：
10.11784/tdxbz202203015
中图分类号：
学科分类号：
摘要：
As the key core component of wind turbines，planetary gearboxes can effectively improve wind power generation efficiency by diagnosing their faults accurately. Being an intricate mechanical component of transmission，the planetary gear system exhibits an exceptionally complex spectral performance，the fault information is easily overwhelmed by irrelevant or disturbing components，and using signal decomposition to obtain fault components plays an important role in planetary gearbox fault diagnosis. Therefore，to address the problem of empirical Fourier decomposition(EFD)easily falling into the local spectrum segmentation，its spectrum segmentation algorithm is optimized，and the boundary threshold mechanism is introduced to the original spectrum segmentation algorithm to optimize the selection of the boundary point of spectrum segmentation and effectively limit the boundary frequency falling into the local problem. A multi-component simulation signal is constructed to compare and analyze the original and the optimized spectrum segmentation algorithms，and the components for the comparison and analysis are gradually increased. The simulation analysis results show that the original spectrum segmentation algorithm gradually falls into the local boundary frequency with the increase of components，while the optimized algorithm can accurately obtain the boundary frequency，thus verifying the effectiveness of the optimized EFD algorithm and showing that the optimized algorithm is an effective improvement on the original spectrum segmentation algorithm. Finally，the experimental data analysis of the wind power planetary gearbox shows that compared with the EFD algorithm，the boundary frequency obtained by the optimized EFD algorithm is not easy to fall into the local area，and the fault components can be better obtained. In the fault diagnosis of the wind power planetary gearbox，it can more effectively identify the fault frequency components and determine the fault location. © 2023 Tianjin University. All rights reserved.
引用
下载
收藏
页码：355 / 360
页数：5
相关论文
共 14 条
  • [1] Jiang Xing, Geng Duyan, Zhang Yuanyuan, Et al., BCG signal de-noising method research based on EMD-ICA[J], Chinese Journal of Biomedical Engineering, 38, 2, pp. 138-145, (2019)
  • [2] Ke Zeng, Dan Chen, Gaoxiang Ouyang, Et al., An EEMD-ICA approach to enhancing artifact rejection for noisy multivariate neural data[J], IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24, 6, pp. 630-638, (2016)
  • [3] Xinyi Yao, Qiuzhan Zhou, Cong Wang, Et al., An adaptive seismic signal denoising method based on variational mode decomposition[J], Measurement, 177, (2021)
  • [4] Jun Pan, Jinglong Chen, Yangyang Zi, Et al., Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment[J], Mechanical Systems and Signal, 72, pp. 160-183, (2016)
  • [5] Shiqian Chen, Yang Yang, Zhike Peng, Et al., Adaptive chirp mode pursuit：Algorithm and applications[J], Mechanical Systems and Signal Processing, 116, pp. 566-584, (2019)
  • [6] Huang N E，, Zheng S，, Long S R，, Et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J], Proceedings of the Royal Society of London A：Mathematical，Physical and Engineering Sciences, 454, 1971, pp. 903-995, (1998)
  • [7] Wu Z, Huang N E., Ensemble empirical mode decomposition：A noise-assisted data analysis method[J], Advances in Adaptive Data Analysis, 1, 1, pp. 1-41, (2009)
  • [8] Doherty J F., Raised cosine filter-based empirical mode decomposition[J], IET Signal Processing, 5, 2, pp. 121-129, (2011)
  • [9] Xu Jinhong, Wei Xiuye, He Yan, Et al., Fault diagnosis of planetary gear based on CEEMDAN sample entropy and PNN[J], Machine Tool & Hydraulics, 49, 20, pp. 179-183, (2021)
  • [10] Gilles J., Empirical wavelet transform[J], IEEE Transactions on Signal Processing, 61, 16, pp. 3999-4010, (2013)
12