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首页> 外文期刊>Journal of Intelligent Manufacturing >Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning
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Fault feature enhancement for rotating machinery based on quality factor analysis and manifold learning

机译：基于质量因子分析和多媒体学习的旋转机械故障特征增强

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摘要

This paper explores an improved time-frequency signature to enhance the periodic transient shocks of the signal, called impulse-enhanced signature (IES) for identifying rotating machine faults. IES is extracted in the following steps: first, phase space reconstruction is applied to the analyzed signal to present its dynamic signature in high-dimensional space; second, employ quality factor (Q-factor) based decomposition on the phase space to separate the fault transient component from the vibration signal; third, utilize the continuous wavelet transform to present nonstationary information embedded in the signal and finally, IES is obtained by optimizing the low-dimension structure, which is extracted from the phase space using manifold learning. The IES significantly improves the fault information with a highly regular representation, especially for weak fault-induced impulses, and its advantages over other approaches include noise suppression and energy concentration. One simple IES based curve, time marginal amplitude (TMA), is extracted to further detect the fault characteristic frequency and evaluate the performance of IES. Simulation and experiments confirm the effectiveness of the proposed method. Results indicate that IES outperforms traditional empirical mode decomposition envelop analysis for diagnosing rotating machine faults.
机译：本文探讨了改进的时频特征，以增强信号的周期性瞬态冲击，称为脉冲增强型签名（IES），用于识别旋转机器故障。在以下步骤中提取IES：首先，将相位空间重建应用于分析的信号，以在高维空间中呈现其动态签名;其次，采用基于质量因子（Q系数）在相位空间上的分解，将故障瞬态分量与振动信号分开;第三，利用连续小波变换来呈现嵌入信号中的非间断信息，最后，通过优化低尺寸结构来获得IE，其使用歧管学习从相空间提取。 IES显着提高了具有高度常规表示的故障信息，特别是对于弱故障引起的冲动，其优于其他方法包括噪声抑制和能量集中。基于一个简单的IES曲线，提取时间边缘幅度（TMA），以进一步检测故障特征频率并评估IE的性能。仿真和实验证实了该方法的有效性。结果表明，IES优于传统的经验模型分解包围分析，用于诊断旋转机器故障。

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