• CN:11-2187/TH
  • ISSN:0577-6686

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

• 机械动力学 • 上一篇    下一篇

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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   

  1. 清华大学机械工程系 北京 100084
  • 收稿日期:2019-03-18 修回日期:2019-09-18 出版日期:2020-02-05 发布日期:2020-04-09
  • 通讯作者: 褚福磊(通信作者),男,1959年出生,博士,教授,博士研究生导师。主要研究方向为旋转机械动力学与振动控制、机械状态监测与故障诊断。E-mail:chufl@mail.tsinghua.edu.cn
  • 作者简介:赵德尊,男,1990年出生,博士后。主要研究方向为旋转机械智能监测与故障诊断。E-mail:14116346@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金(51905292),中国博士后科学基金(2019M660613)和现代测控技术教育部重点实验室开放课题(KF20191123203)资助项目。

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

ZHAO Dezun, WANG Tianyang, CHU Fulei   

  1. Department of Mechanical Engineering, Tsinghua University, Beijing 100084
  • Received:2019-03-18 Revised:2019-09-18 Online:2020-02-05 Published:2020-04-09

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。

关键词: 滚动轴承, 故障诊断, 时变非平稳, 自适应广义解调变换, 广义特征指标

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.

Key words: rolling bearing, fault diagnosis, time-varying nonstationary, adaptive generalized demodulation transform, generalized characteristic index

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