基于自适应广义解调变换的滚动轴承时变非平稳故障特征提取

doi:10.3901/JME.2020.03.080

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机械工程学报 ›› 2020, Vol. 56 ›› Issue (3) : 80-87. DOI: 10.3901/JME.2020.03.080

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机械动力学

基于自适应广义解调变换的滚动轴承时变非平稳故障特征提取

赵德尊, 王天杨, 褚福磊

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Adaptive Generalized Demodulation Transform Based Rolling Bearing Time-varying Nonstationary Fault Feature Extraction

ZHAO Dezun, WANG Tianyang, CHU Fulei

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摘要

变转速运行工况下滚动轴承故障冲击幅值与故障冲击间隔表现为时变性。现有的研究主要集中于解决时变冲击间隔带来的频谱模糊问题，鲜有对时变冲击幅值问题的探究。低转速下，故障冲击幅值较小，容易淹没在噪声中，给轴承故障诊断带来困难。据此，提出了基于自适应广义解调变换（Generalized demodulation transform，GDT）的滚动轴承时变非平稳故障特征提取方法。定义了重置准则优化GDT，使其转换因子可调、重置因子最优，时变的故障相关的频率曲线自适应的聚集于最高点，而非起始点。从故障轴承振动信号的高频共振成分的包络时频谱中提取时频脊线，结合假设思想构建了广义特征指标（Generalized characteristic index，GCI）模型。基于自适应GDT实现故障相关时频脊线的平稳化重置，进而通过频谱量化表征与GCI模型无需转速测量完成轴承故障诊断。仿真和实测信号分析结果表明所提方法的有效性。

Abstract

Fault impulse amplitude and fault impulse intervals of rolling bearing are time-varying under nonstationary conditions. The existing research mainly focuses on solving the problem of spectral smearing caused by time-varying impulse intervals, and few research on the problem of time-varying impulse amplitude. Under low rotational speeds, the magnitude of the fault impulses is small, and it is easily overwhelmed by noise, which makes it difficult for rolling bearing fault diagnosis. As such, an adaptive generalized demodulation transform (GDT) based rolling bearing fault diagnosis method under nonstationary conditions is proposed. The reset criterion is developed to improve the GDT, whose conversion factor is adjustable and the reset factor is optimal. As a result, the time-varying fault-related frequency curve is adaptively concentrated on the highest point instead of the starting point. The generalized characteristic index (GCI) model is defined via the hypothesis and the time-frequency ridge, extracted from the envelope time-frequency representation of filtered signal. The fault-related frequency ridge is transformed by the adaptive GDT, combined with spectrum based quantitative characterization and the GCI, the rolling bearing is diagnosed without the rotational speed measurement. The analysis results of simulated and measured signal demonstrate the effectiveness of the proposed method.

导出引用

赵德尊, 王天杨, 褚福磊. 基于自适应广义解调变换的滚动轴承时变非平稳故障特征提取[J]. 机械工程学报, 2020, 56(3): 80-87 https://doi.org/10.3901/JME.2020.03.080

ZHAO Dezun, WANG Tianyang, CHU Fulei. Adaptive Generalized Demodulation Transform Based Rolling Bearing Time-varying Nonstationary Fault Feature Extraction[J]. Journal of Mechanical Engineering, 2020, 56(3): 80-87 https://doi.org/10.3901/JME.2020.03.080

参考文献

[1] BORGHESANI P,RICCI R,CHATTERTON S,et al. A new procedure for using envelope analysis for rolling element bearing diagnostics in variable operating conditions[J]. Mechanical Systems and Signal Processing, 2013,38:23-35.
[2] RANDALL R B,ANTONI J. Rolling element bearing diagnostics-a tutorial[J]. Mechanical Systems and Signal Processing,2011,25:485-520.
[3] BOOSLEY K M,MCKENDRICK R J,HARRIS C J,et al. Hybrid computed order tracking[J]. Mechanical Systems and Signal Processing,1999,13:627-641.
[4] 赵晓平,张令弥,郭勤涛. 旋转机械阶比跟踪技术研究进展综述[J]. 地震工程与工程振动,2008,28(6):213-219. ZHAO Xiaoping,ZHANG Lingmi,GUO Qintao. Advances and trends in rotational machine order tracking methodology[J]. Journal of Earthquake Engineering and Engineering Vibration,2008,28(6):213-219.
[5] GUO Y,LIU T,NA J,et al. Envelope order tracking for fault detection in rolling element bearings[J]. Journal of Sound and Vibration,2012,331(25):5644-5654.
[6] 郝高岩,刘永强,廖英英. 一种基于改进阶次包络谱的滚动轴承故障诊断算法[J]. 振动与冲击,2016,35(15):144-148. HAO Gaoyan,LIU Yongqiang,LIAO Yingying. A rolling bearing fault diagnosis algorithm based on improved order envelope spectrum[J]. Journal of Vibration and Shock,2016,35(15):144-148.
[7] 王天杨,李建勇,程卫东. 基于低次故障特征阶比系数的变转速滚动轴承等效转频估计算法[J]. 机械工程学报,2015,51(3):121-128. WANG Tianyang,LI Jianyong,CHENG Weidong. Equivalent rotational frequency estimation algorithm of faulty rolling bearing under varying rotational speed based on the lower fault characteristic order coefficient[J]. Journal of Mechanical Engineering,2015,51(3):121-128.
[8] SAAVEDRA P N,RODRIGUEZ C G. Accurate assessment of computed order tracking[J]. Shock and Vibration,2006,13(1):13-21.
[9] CHENG W,GAO R X,WANG J,et al. Envelope deformation in computed order tracking and error in order analysis[J]. Mechanical System and Signal Processing,2014,48(1-2):92-102.
[10] ZHAO D,LI J,CHENG W,et al. Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed[J]. Journal of Sound and Vibration,2016,378:109-123.
[11] OLHEDE S,WALDEN A T. A generalized demodulation approach to time-frequency projections for multicomponent signals[J]. Proceedings of the Royal Society A,2005,461(2059):2159-2179.
[12] WANG T,LIANG M,LI J,et al. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis[J]. Mechanical Systems and Signal Processing,2014,45(1):139-153.
[13] SHI J,LIANG M,GUAN Y. Bearing fault diagnosis under variable rotational speed via the joint application of windowed fractal dimension transform and generalized demodulation:a method free from prefiltering and resampling[J]. Mechanical Systems and Signal Processing,2016,68:15-33.