A Higher-Order Symmetric Difference Analytic Energy Operator and its Applications to Detect Weak Bearing Fault Signal

被引:0
|
作者
Xu Y.-B. [1 ]
Jiang X.-K. [1 ]
Li L. [1 ]
机构
[1] School of Automation, Xi'an University of Posts and Telecommunications, Xi'an
来源
关键词
Analytic energy operator; Bearing fault signature detection; Higher-order symmetric difference; Non-negative property;
D O I
10.12263/DZXB.20200201
中图分类号
TH13 [机械零件及传动装置];
学科分类号
080203 ;
摘要
Considering that the inadequacy of the inferior performance of the symmetric difference analytic energy operator (SD-AEO) technique in the presence of vibration interferences with higher amplitudes, an improved analytic energy operator (AEO) based on SD-AEO is proposed in this work.This proposed energy measure takes advantage of the higher-order symmetric difference to substitute the symmetric difference for the sake of boosting the signal-to-interference ratio (SIR), which can enhance the ability to detect the weak fault signal in the presence of vibration interferences with larger magnitude.Furthermore, the energy transformation of this proposed energy operator is weighted by absolute value to prevent the negative values in the transformed signal.Under the simulated tests, the superiority of the immunity to intensive noise and interferences as well as the non-negative property is demonstrated.Finally, the proposed energy detector is applied to the real-world data measured from the defective bearings, and it is found that this proposed fault detection tool can successfully identify the weak fault signatures under a severe working condition. © 2021, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码:750 / 759
页数:9
相关论文
共 19 条
  • [1] Yang D-M, Stronach A F, MacConnell P, The application of advanced signal processing techniques to induction motor bearing condition diagnosis, Meccanica, 38, 2, pp. 297-308, (2003)
  • [2] Zheng J, Cheng J, Yang Y., Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis, Mechanical Systems and Signal Processing, 40, 1, pp. 136-153, (2013)
  • [3] SU Wei-jun, YANG Fei, YU Chong-chong, Rolling bearing fault feature extraction method based on local spectrum, Acta Electronica Sinica, 46, 1, pp. 160-166, (2018)
  • [4] Pineda-Sanchez M, Puche-Panadero R, Riera-Guasp M, Et al., Application of the teager-kaiser energy operator to the fault diagnosis of induction motors, IEEE Transactions on Energy Conversion, 28, 4, pp. 1036-1044, (2013)
  • [5] WANG Tian-jin, FENG Zhi-peng, HAO Ru-jiang, Fault diagnosis of rolling element bearings based on Teager energy operator, Journal of Vibration and Shock, 31, 2, pp. 1-5, (2012)
  • [6] GAI Qiang, ZHANG Hai-yong, XU Xiao-gang, Study of adaptive frequency multiresolution analysis of the hilbert-huang transform, Acta Electronica Sinica, 33, 3, pp. 563-566, (2005)
  • [7] WU Zhe, YANG Shao-pu, ZHANG Jian-chao, Bearing fault feature extraction method based on LMD adaptive multiscale morphology and energy operator demodulating, Journal of Vibration and Shock, 35, 3, pp. 7-13, (2016)
  • [8] Bovik A C, Maragos P, Quatieri T F., AM-FM energy detection and separation in noise using multiband energy operators, IEEE Transactions on Signal Processing, 41, 12, pp. 3245-3265, (1993)
  • [9] LIU Ze-chao, ZHANG Bing, YI Cai, WU Wen-yi, HUANG Cheng-uang, High-order frequency-weighted energy operator with applications to train axle-box bearing fault diagnosis, Journal of Xi'an Jiaotong University, 53, 12, pp. 46-56, (2019)
  • [10] Faghidi H, Liang M, Bearing fault identification by higher order energy operator fusion: A non-resonance based approach, Journal of Sound and Vibration, 381, pp. 83-100, (2016)