Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

被引:1
|
作者
Xu, Xiangyang [1 ]
Liu, Guanrui [1 ]
Liang, Xihui [2 ]
机构
[1] Chongqing Jiaotong Univ, Sch Mech & Vehicle Engn, Chongqing 400074, Peoples R China
[2] Univ Manitoba, Dept Mech Engn, Winnipeg, MB T3T 2N2, Canada
关键词
planetary gear dynamics; magnetomotive force; air gap magnetic field; induction motor; fault detection; MOTOR CURRENT; AIRGAP ECCENTRICITY; DIAGNOSIS; VIBRATION;
D O I
10.3390/machines9110277
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Fault Diagnosis Method of Planetary Gearbox Based on Compressed Sensing and Transfer Learning
    Bai, Huajun
    Yan, Hao
    Zhan, Xianbiao
    Wen, Liang
    Jia, Xisheng
    [J]. ELECTRONICS, 2022, 11 (11)
  • [32] An adaptive, on-line, statistical method for bearing fault detection using stator current
    Yazici, B
    Kliman, GB
    Premerlani, WJ
    Koegl, RA
    Robinson, GB
    AbdelMalek, A
    [J]. IAS '97 - CONFERENCE RECORD OF THE 1997 IEEE INDUSTRY APPLICATIONS CONFERENCE / THIRTY-SECOND IAS ANNUAL MEETING, VOLS 1-3, 1997, : 213 - 220
  • [33] Planetary gearbox fault feature enhancement based on combined adaptive filter method
    Tian, Shuangshu
    Qian, Zheng
    [J]. ADVANCES IN MECHANICAL ENGINEERING, 2015, 7 (12):
  • [34] An adaptive stochastic resonance method for weak fault characteristic extraction in planetary gearbox
    Li, Zhixing
    Shi, Boqiang
    [J]. JOURNAL OF VIBROENGINEERING, 2017, 19 (03) : 1782 - 1792
  • [35] Fault detection in a multistage gearbox by demodulation of motor current waveform
    Mohanty, A. R.
    Kar, Chinmaya
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2006, 53 (04) : 1285 - 1297
  • [36] A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion
    Wu, Zhe
    Zhang, Qiang
    Cheng, Lifeng
    Tan, Shengyue
    [J]. APPLIED SCIENCES-BASEL, 2019, 9 (24):
  • [37] Adaptive Estimation of Instantaneous Angular Speed for Wind Turbine Planetary Gearbox Fault Detection
    Wang, Yi
    Tang, Baoping
    Meng, Lihua
    Hou, Bingchang
    [J]. IEEE ACCESS, 2019, 7 : 49974 - 49984
  • [38] Research on the enhancement of the fault feature of the outer raceway of the sun gear bearing of a planetary gearbox
    Xiao, Fei
    Xiong, Xiaoyan
    Niu, Linkai
    Xie, Honghao
    Zhang, Wei
    Zheng, Yizhen
    Qi, Hortgwei
    [J]. Zhendong yu Chongji/Journal of Vibration and Shock, 2022, 41 (20): : 142 - 150
  • [39] Intelligent Detection of a Planetary Gearbox Composite Fault Based on Adaptive Separation and Deep Learning
    Sun, Guo-dong
    Wang, You-ren
    Sun, Can-fei
    Jin, Qi
    [J]. SENSORS, 2019, 19 (23)
  • [40] Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network
    Jiang Lingli
    Li Shuhui
    Li Xuejun
    Lei Jiale
    Yang Dalian
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (04)