A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines

被引:53
作者
Feng, Ke [1 ]
Ji, J. C. [1 ]
Wang, Kesheng [2 ]
Wei, Dongdong [3 ]
Zhou, Chengning [4 ]
Ni, Qing [1 ]
机构
[1] Univ Technol Sydney, Sch Mech & Mechatron Engn, Sydney, NSW 2007, Australia
[2] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China
[3] Univ Alberta, Dept Mech Engn, Edmonton, AB T6G 1H9, Canada
[4] Nucl Power Inst China, Shenzhen, Peoples R China
关键词
Offshore wind turbines; Vold -Kalman filter order tracking; Characteristic vibrations; Bandwidth; Condition monitoring process; Planetary gearbox; PLANETARY GEARBOX; TRACKING; VIBRATION;
D O I
10.1016/j.oceaneng.2022.112920
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Vold-Kalman order tracking filter is an effective technique for dealing with non-stationary vibrations which offshore wind turbines often encounter. It has a unique capability to extract and track the time waveforms of harmonics in short transients without phase bias, and this capability is beneficial to the condition monitoring of offshore wind turbines. In general, the accuracy of the tracking results of the Vold-Kalman filer for condition monitoring is heavily dependent on the selection of filter bandwidth. A fixed filter bandwidth becomes prob-lematic when processing different types of signals under varying operating conditions. Significant errors may arise in the tracking, rendering the condition monitoring of offshore wind turbines unreliable. To address this issue, this paper proposes a novel scheme for Vold-Kalman filter bandwidth selection to guarantee the consis-tency and accuracy of the offshore wind turbine condition monitoring process, ensuring reliable fault diagnosis. A numerical model is used to evaluate the effectiveness of the proposed bandwidth selection scheme first. Then the proposed scheme is further validated through the offshore wind turbine planetary gearbox datasets, together with the demonstration of the fault diagnosis capability of the filtered results.
引用
收藏
页数:14
相关论文
共 37 条
[1]   Unsupervised noise cancellation for vibration signals: part II - a novel frequency-domain algorithm [J].
Antoni, J ;
Randall, RB .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (01) :103-117
[2]   A survey of DSP methods for rotating machinery analysis, what is needed, what is available [J].
Blough, JR .
JOURNAL OF SOUND AND VIBRATION, 2003, 262 (03) :707-720
[3]   Single variable shear deformation theory for free vibration and harmonic response of frames on flexible foundation [J].
Bozyigit, Baran ;
Yesilce, Yusuf ;
Wahab, Magd Abdel .
ENGINEERING STRUCTURES, 2020, 208
[4]   Free vibration and harmonic response of cracked frames using a single variable shear deformation theory [J].
Bozyigit, Baran ;
Yesilce, Yusuf ;
Wahab, Magd Abdel .
STRUCTURAL ENGINEERING AND MECHANICS, 2020, 74 (01) :33-54
[5]   Transfer matrix formulations and single variable shear deformation theory for crack detection in beam-like structures [J].
Bozyigit, Baran ;
Yesilce, Yusuf ;
Wahab, Magd Abdel .
STRUCTURAL ENGINEERING AND MECHANICS, 2020, 73 (02) :109-121
[6]  
Brandt A, 2005, SOUND VIB, V39, P19
[7]   A review of vibration-based gear wear monitoring and prediction techniques [J].
Feng, Ke ;
Ji, J. C. ;
Ni, Qing ;
Beer, Michael .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 182
[8]   Vibration-based monitoring and prediction of surface profile change and pitting density in a spur gear wear process [J].
Feng, Ke ;
Smith, Wade A. ;
Randall, Robert B. ;
Wu, Hongkun ;
Peng, Zhongxiao .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 165
[9]   Use of cyclostationary properties of vibration signals to identify gear wear mechanisms and track wear evolution [J].
Feng, Ke ;
Smith, Wade A. ;
Borghesani, Pietro ;
Randall, Robert B. ;
Peng, Zhongxiao .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 150
[10]   Vibration-based updating of wear prediction for spur gears [J].
Feng, Ke ;
Borghesani, Pietro ;
Smith, Wade A. ;
Randall, Robert B. ;
Chin, Zhan Yie ;
Ren, Jinzhao ;
Peng, Zhongxiao .
WEAR, 2019, 426 :1410-1415