Rotor vibration signal recognition method based on coupling source error analysis

被引:0
|
作者
Li S. [1 ]
Liu Y. [1 ]
Lin Z. [1 ]
Shutin D. [2 ]
Luo Y. [1 ]
机构
[1] School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen
[2] Department of Mechanical Electronics and Robotics, Orel State University n. a. I. S. Turgenev, Orel
关键词
coupling source; Fourier fitting; rotor; vibration signal identification;
D O I
10.19650/j.cnki.cjsi.J2210435
中图分类号
O441.1 [电学]; TM12 [];
学科分类号
摘要
Real-time monitoring of the rotor vibration signal is a key to ensure rotating machinery running steadily. The coupling source error of rotor roundness error and eddy current displacement sensor error is rarely considered in previous research and vibration monitoring, which causes distortion of the rotor vibration signal, and even causes misjudgment. Taking an actual rotor as an example, a measurement expression of the roundness error is proposed. The errors of two common eddy current displacement sensors are measured and analyzed. The expression of source error which is coupled by roundness error and eddy current displacement sensor error is constructed by Fourier fitting. The mapping relationship between coupling source error and rotor vibration signal is established. Three rotor vibration signal recognition methods are proposed, including the point-point method, the average value method and the max value method. All three methods can effectively recognize the rotor vibration signal. The point-point method is the most accurate, and its recognize error accounts for about 20% . The average value method is simple to calculate, and its recognize error accounts for about 10% . The maximum value method is conservative. However, it helps to effectively avoid misjudgment and its recognize error accounts for about 32% . © 2023 Science Press. All rights reserved.
引用
收藏
页码:75 / 83
页数:8
相关论文
共 16 条
  • [1] WU X Y, ZHANG Y, CHENG C M, Et al., A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery[J], Mechanical Systems and Signal Processing, 149, (2021)
  • [2] ZHAO B, LI H., Noise reduction method of vibration signal combining EMD and LSF, Journal of Vibration, Measurement & Diagnosis, 42, 3, pp. 606-610, (2022)
  • [3] ZHANG Y Q, ZHANG P L, WANG H G, Et al., Rolling bearing early fault intelligence recognition based on weak fault feature enhancement in time-time domain, Journal of Mechanical Engineering, 52, 21, pp. 96-103, (2016)
  • [4] XU Q Q, LIN M H, LIU K, Et al., Fractional lower order feature extraction method of PF components of rolling bearings, Journal of Vibration, Measurement & Diagnosis, 40, 6, pp. 1141-1149, (2020)
  • [5] QU J L, YU L, YUAN T, Et al., Adaptive fault diagnosis algorithm for rolling bearings based on one-dimensional convolutional neural network [J], Chinese Journal of Scientific Instrument, 39, 7, pp. 134-143, (2018)
  • [6] MA L, KANG J SH, MENG Y, Et al., Research on feature extraction of rolling bearing incipient fault based on Morlet wavelet transform [J], Chinese Journal of Scientific Instrument, 34, 4, pp. 920-926, (2013)
  • [7] HOU ZH Q, XIONG W L, LYU L, Et al., Study on the influence of the journal shape error for hydrostatic spindle rotational error motion, Journal of Mechanical Engineering, 52, 15, pp. 147-154, (2016)
  • [8] MAHDAL M, LOS J, ZAVADIL J., Verification method of rotors instability measurement, Proceedings of the 14th International Carpathian Control Conference (ICCC), pp. 228-231, (2013)
  • [9] LI S, ZHOU C J, SAVIN L, Et al., Theoretical and experimental study of motion suppression and friction reduction of rotor systems with active hybrid fluid-film bearings[J], Mechanical Systems and Signal Processing, 182, (2023)
  • [10] YANG H R, ZHAO T, SUN X W, Et al., Research on radial rotation error separation technology of machine tool spindle based on the improved genetic algorithm, Chinese Journal of Scientific Instrument, 42, 1, pp. 82-91, (2021)