中国机械工程 ›› 2022, Vol. 33 ›› Issue (14): 1697-1706.DOI: 10.3969/j.issn.1004-132X.2022.14.008

• 智能制造 • 上一篇    下一篇

改进的共振稀疏分解方法及其在滚动轴承复合故障诊断中的应用

张守京;慎明俊;杨静雯;吴芮   

  1. 西安工程大学机电工程学院,西安,710600
  • 出版日期:2022-07-25 发布日期:2022-08-02
  • 作者简介:张守京,男,1976年生,博士,副教授,硕士研究生导师。主要研究方向为设备维修维护与健康管理。发表论文20篇。E-mail:zhangshoujing@xpu.edu.cn。
  • 基金资助:
    国家重点研发计划(2019YFB1707205)

Improved RSSD and Its Applications to Composite Fault Diagnosis of Rolling Bearings

ZHANG Shoujing;SHEN Mingjun;YANG Jingwen;WU Rui   

  1. School of Mechanical and Electrical Engineering,Xian Polytechnic University,Xian,710600
  • Online:2022-07-25 Published:2022-08-02

摘要: 滚动轴承复合故障信号中各故障特征受到传输路径和其他干扰源的影响,在多缺陷共存条件下提取单个缺陷诱发的故障特征存在困难。提出一种基于双参数优化、子带重构改进的共振稀疏分解(RSSD)滚动轴承复合故障诊断方法:首先利用人工鱼群算法自适应选择RSSD的品质因子和分解层数以构造与故障特征匹配的最优小波基,获得包含瞬态冲击的低共振分量;然后依据提出的子带筛选准则选择并重构低共振分量中包含瞬态冲击成分的最佳子带;最后通过多点最优最小熵反卷积(MOMEDA)方法识别并提取重构信号中周期性故障冲击。仿真信号和轴承全寿命周期复合故障信号分析结果表明,与RSSD-MCKD方法相比,所提出方法能有效提取复合故障信号中各故障特征,精确实现轴承复合故障诊断。

关键词: 共振稀疏分解, 品质因子, 子带重构, 多点最优最小熵反卷积

Abstract: Due to the influences of transmission paths and various interference sources, the individual defect-induced fault features of bearings simultaneously arising from multiple defects were difficult to extract from vibration signals, an improved RSSD method was proposed, which was combined with dual-parameter optimization and subband reconstruction. Firstly, the Q factor of RSSD and the number of decomposition layers were adaptively selected using the artificial fish swarm algorithm to construct the optimal wavelet basis matching the fault features and to obtain the low resonance components containing transient components. Secondly, the optimum sub-band which carried transient feature information, was selected and reconstructed using the proposed subband screening principle. Finally, the periodic impulses of the composite fault signals were identified and extracted by MOMEDA method. The analysis on the simulated signals and the experimental composite fault signals in the bearing life cycle shows that the proposed method may effectively extract each fault feature from the composite fault signals, and accurately realize the composite fault diagnosis compared with RSSD-maximum correlation kurtosis deconvolution(RSSD-MCKD) method.

Key words: resonance-based sparse signal decomposition(RSSD), quality factor(Q-factor), subband reconstruction, multipoint optimal minimum entropy deconvolution adjusted(MOMEDA)

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