基于自适应SSA和改进TEO的船用消防泵电机轴承故障特征增强与诊断

Marine fire pump motor bearings fault feature enhancement and diagnosis based on adaptive SSA and improved TEO

  • 摘要:
    目的 船用消防泵电机的轴承工作环境较复杂,其故障诊断的准确度较低,为此提出一种基于自适应稳态子空间分析(SSA)与改进Teager能量算子(TEO)的船用消防泵电机轴承故障特征增强与诊断方法。
    方法 首先,对传统SSA方法进行优化,采用虚假最近邻点确定Hankel矩阵维数,并基于峭度指标提取振动信号中经SSA分解后的包含最佳故障特征的非稳态信号;然后,通过改进TEO算法,有效提升故障特征在振动信号中的占比,从而增强故障特征并实现准确诊断;最后,通过仿真实验和工程实验进行有效性验证。
    结果 对比结果表明,该方法可以准确地判断轴承的故障特征频率及其倍频,从而有效诊断轴承故障。
    结论 研究成果可为船用泵组电机轴承的故障诊断提供参考。

     

    Abstract:
    Objectives The working environment of marine fire pump motor bearings is complex with low fault diagnosis accuracy. To address these issues, this study proposes a fault feature enhancement and diagnosis method for marine fire pump motor bearings based on adaptive steady-state subspace analysis (SSA) and improved Teager energy operator (TEO).
    Methods  First, the traditional SSA algorithm is optimized to adaptively determine the dimensionality of the Hankel matrix by the false nearest neighbor method, and non-stationary signals with the best fault features are extracted from the vibration signal through kurtosis. Second, by improving the TEO algorithm, the proportion of fault feature information in the vibration signals is increased, fault features are enhanced and faults are diagnosed. Finally, the effectiveness of the method is verified through simulation and engineering experiments.
    Results The proposed method can accurately distinguish the fault characteristic frequency and harmonics of bearings, and accurately diagnose bearing faults.
    Conclusions The results of this study can provide references for the fault diagnosis of marine pump motor bearings.

     

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