基于循环谱相关的编码器信号滚动轴承故障检测

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摘要针对振动传感器安装受限的场合，结合编码器信号的优势，以编码器瞬时角速度(Instantaneous angular speed, IAS)为信号，提出一种基于编码器IAS信号诊断特征（Diagnostic Feature, DF）指标的循环谱相关(Cyclic Spectral Correlation, CSC)优化解调频带选取算法。首先利用向前差分法估计编码器信号获得轴承的IAS信号，并利用CSC得到IAS信号的双变量谱；然后按照初始子频带带宽为循环频率积分区间得到子频带改进包络谱(Improved Envelope Spectrum, IES)，并计算子频带IES的DF数值获得DF曲线；再通过评判DF数值合并子频带得到优化解调频带；最后利用包络分析提取滚动轴承故障特征阶次。通过仿真和轴承实测数据验证了本文所提方法的有效性。

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	田田
	郭瑜
	杨新敏
	邹翔
	陈鑫

关键词 ：滚动轴承, 编码器, 瞬时角速度, IESFOgram, 自适应频带划分

Abstract：In view of the limited installation of vibration sensors, combined with the advantages of the encoder signal, a cyclic spectrum correlation (CSC) optimization demodulation frequency band selection algorithm based on the encoder instantaneous angular speed (IAS) signal diagnostic feature (DF) index is proposed with the encoder IAS as the signal. Firstly, the encoder signal is estimated by the forward difference method to obtain the IAS signal of the bearing, and the bivariate spectrum of the IAS signal is obtained by using the CSC; then the sub-band improved envelope spectrum (IES) is obtained according to the initial sub-band bandwidth as the cyclic frequency integration interval, and the sub-band is calculated. The DF value of the IES is obtained to obtain the DF curve; the optimal demodulation frequency band is obtained by combining the sub-bands by judging the DF value; finally, the fault characteristic order of the rolling bearing is extracted by the envelope analysis. The effectiveness of the method proposed in this paper is verified by simulation and bearing measured data.

Key words： rolling bearing encoder instantaneous angular speed IESFOgram adaptive frequency band division

收稿日期: 2022-09-23 出版日期: 2023-08-28

引用本文:

田田，郭瑜，杨新敏，邹翔，陈鑫. 基于循环谱相关的编码器信号滚动轴承故障检测[J]. 振动与冲击, 2023, 42(16): 202-208.
TIAN Tian,GUO Yu,YANG Xinmin,ZOU Xiang,CHEN Xin. Fault detection of rolling bearings based on encoder signals via cyclic spectral correlation. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(16): 202-208.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I16/202

[1] Miao Y, Zhao M, Liang K, et al. Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal[J]. Renewable Energy, 2019.
[2] Renaudin L, Bonnardot F, Musy O, et al. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed[J]. Mechanical Systems and Signal Processing, 2010, 24(7): 1998-2011.
[3] Moustafa W, Cousinard O, Bolaers F, et al. Low speed bear-ings fault detection and size estimation using instantaneous angular speed[J]. Journal of Vibration and Control, 2016, 22(15): 3413-3425.
[4] Gomez J L, Bourdon A, André H, et al. Modelling deep groove ball bearing localized defects inducing instantaneous angular speed variations[J]. Tribology International, 2016, 98: 270-281.
[5] Randall R B, Antoni J, Chobsaard S. The relationship betwees spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals[J]. Mechanical Systems and Signal Processing, 2001, 15(5): 945-962.
[6] 徐可君, 任帅, 秦海勤, 等. 基于QGA和SK的滚动轴承故障诊断新方法[J]. 振动与冲击, 2015, 34(11): 105-109.
Xu K J, Ren S, Qin H Q, et al. Rolling bearing fault diagnosis based on quantum genetic algorithm and spectral kurtosis[J]. Journal of Vibration and Shock, 2015, 34(11): 105-109.
[7] Antoni J. Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions[J]. Journal of Sound and Vibration, 2007, 304: 497-529.
[8] Antoni J, Bonnardot F, Raad A, et al. Cyclostationary model-ling of rotating machine vibration signals[J]. Mechanical Systems and Signal Processing, 2004, 18(6): 1285-1314.
[9] Antoni J, Xin G, Hamzaoui N. Fast computation of the spectral correlation[J]. Mechanical Systems and Signal Processing, 2017, 92: 248-277.
[10] Mauricio A, Smith W, Randall R B, et al. Improved Envelope Spectrum via Feature Optimisation-gram (IESFOgram): A novel tool for rolling element bearing diagnostics under non-stationary operating conditions[J]. Mechanical Systems and Signal Processing, 2020, 144: 106891.
[11] Mauricio A, Gryllias K. Cyclostationary-based Multiband Envelope Spectra Extraction for bearing diagnostics: The Combined Improved Envelope[J]．Mechanical systems and signal processing, 2021,149:107150.
[12] 陈鑫, 郭瑜, 伍星, 等. 改进IESFOgram的滚动轴承故障特征提取[J]. 振动工程学报, 2021, 34(04): 861-868.
Chen X, Guo Y, Wu X, et al. Feature extraction of faulty roll-ing element bearing based on improved IESFOgram[J]. Jour-nal of Vibration Engineering, 2021, 34(04): 861-868.
[13] Wang Y, Ming L. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2011, 25(5): 750-1764.
[14] 冯志鹏, 褚福磊. 行星齿轮箱故障诊断的扭转振动信号分析方法[J]. 中国电机工程学报, 2013, 33(14): 101-106.
Feng Z P, Chu F L. Torsional Vibration Signal Analysis Method for Fault Diagnosis of Planetary Gearboxes[J]. Pro-ceedings of the CSEE, 2013, 33(14): 101-106.