A New Order Tracking Method for Fault Diagnosis of Gearbox under Non-Stationary Working Conditions Based on In Situ Gravity Acceleration Decomposition

被引:0
|
作者
Li, Yanlei [1 ]
Chen, Zhongyang [2 ,3 ]
Wang, Liming [2 ,3 ]
机构
[1] Southwest Jiaotong Univ, State Key Lab Tract Power, Chengdu 610031, Peoples R China
[2] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[3] Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 11期
关键词
order tracking; fault diagnosis; CEEMDAN; Hilbert transformation; SPEED ESTIMATION; INSTANTANEOUS SPEED; ROTATIONAL SPEED; ALGORITHM;
D O I
10.3390/app14114742
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Rotational speed measuring is important in order tracking under non-stational working conditions. However, sometimes, encoders or coded discs are not easy to mount due to the limited measurement environment. In this paper, a new in situ gravity acceleration decomposition method (GAD) is proposed for rotational speed estimation, and it is applied in the order tracking scene for fault diagnosis of a gearbox under non-stationary working conditions. In the proposed method, a MEMS accelerometer is locally embedded on the rotating shaft or disc in the tangential direction. The time-varying gravity acceleration component is sensed by the in situ accelerometer during the rotation of the shaft or disc. The GAD method is established to exploit the gravity acceleration component based on the linear-phase finite impulse response (FIR) filter and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. Then, the phase signal of time-varying gravity acceleration is derived for rotational speed estimations. A motor-shaft-disc experimental setup is established to verify the correctness and effectiveness of the proposed method in comparison to a mounted encoder. The results show that both the estimated average and instantaneous rotational speed agree well with the mounted encoder. Furthermore, both the proposed GAD method and the traditional vibration-based tacholess speed estimation methods are applied in the context of order tracking for fault diagnosis of a gearbox. The results demonstrate the superiority of the proposed method in the detection of tooth spalling faults under non-stationary working conditions.
引用
收藏
页数:23
相关论文
共 50 条
  • [41] A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions
    Hu, Zhongshuo
    Li, Qiang
    Yang, Jianwei
    Yao, Dechen
    Wang, Jinhai
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (06)
  • [42] A New Fault Feature Extraction Method for Non-Stationary Signal Based on Advanced Synchrosqueezing Transform
    Yu Xin
    Shunming Li
    Jinrui Wang
    Journal of Vibration Engineering & Technologies, 2019, 7 : 291 - 299
  • [43] A New Fault Feature Extraction Method for Non-Stationary Signal Based on Advanced Synchrosqueezing Transform
    Xin, Yu
    Li, Shunming
    Wang, Jinrui
    JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES, 2019, 7 (03) : 291 - 299
  • [44] A New Approach for Vibration-based Rolling Bearings Fault Detection in Non-Stationary Operating Conditions
    Golafshan, Reza
    Wegerhoff, Matthias
    Jacobs, Georg
    Sanliturk, Kenan Y.
    SCHWINGUNGEN 2017: BERECHNUNG, UBERWACHUNG, ANWENDUNG, 2017, 2295 : 347 - 361
  • [45] Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions
    Xu, Yadong
    Chen, Yuejian
    Zhang, Hengcheng
    Feng, Ke
    Wang, Yulin
    Yang, Chunsheng
    Ni, Qing
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 203
  • [46] A fault diagnosis method for rolling bearings based on RDDAN under multivariable working conditions
    Shi, Huaitao
    Gan, Chunxia
    Zhang, Xiaochen
    Meng, Weiying
    Huang, Chengzhuang
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (02)
  • [47] A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions
    Zhu, Jun
    Chen, Nan
    Shen, Changqing
    IEEE SENSORS JOURNAL, 2020, 20 (15) : 8394 - 8402
  • [48] Intelligent fault diagnosis of planetary gearbox based on adaptive normalized CNN under complex variable working conditions and data imbalance
    Wang, Chaoge
    Li, Hongkun
    Zhang, Kongliang
    Hu, Shaoliang
    Sun, Bin
    MEASUREMENT, 2021, 180
  • [49] A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions
    Wu, Binyun
    Hou, Liang
    Wang, Shaojie
    Lian, Xiaozhen
    JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2024, 24 (02)
  • [50] Fault diagnosis method of bearing under cross working conditions based on substructure optimal transmission
    Zhu L.
    Cui Q.
    Hu C.
    He S.
    Zhendong yu Chongji/Journal of Vibration and Shock, 2023, 42 (07): : 273 - 280and332