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Distance-based analysis of dynamical systems reconstructed from vibrations for bearing diagnostics

机译:基于振动的动力系统基于距离的分析,用于轴承诊断

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摘要

Nonlinear fault responses are common in industrial systems yet cannot be effectively extracted by traditional feature extraction methods. In recent years, more techniques based on nonlinear dynamical system reconstruction are reported in the fault diagnosis and prognosis context. However, the key phrases researchers used vary from area to area, and it is difficult to locate the relevant papers. In this paper, we connect the related bearing fault diagnostics and prognostics literature in a short review. We propose a method for reconstructing dynamical system based on time-delay embedding and use it on bearing fault diagnostics. Based on one Wasserstein distance, the earth mover's distance (EMD), we compare the reconstructed bearing vibration data with a baseline reconstruction from normal (healthy) bearing data to generate a severity index over time. Fault type assessment is performed by visualizing the distances between different reconstructed dynamical systems in a two-dimensional plot of the multidimensional scaling (MDS) results. We illustrate the proposed method with two laboratory bearing degradation datasets. We compare the trend with statistical features obtained from time and frequency domain, as well as the EMD trend without using phase space reconstruction. The EMD of reconstructions shows large difference between incipient faults and normal data, and then decreases to the normal bearing level. The EMD ratio is proposed as a fault severity indicator based on shape similarity information not available in the raw data features. The MDS results provide good visualization of the distances among time series for clustering and fault type diagnosis.
机译:非线性故障响应在工业系统中很常见,但不能通过传统的特征提取方法有效地提取。近年来,在故障诊断和预后方面报道了更多基于非线性动力学系统重构的技术。但是,研究人员使用的关键短语因地区而异,并且很难找到相关论文。在本文中,我们将简要回顾相关的轴承故障诊断和预后文献。我们提出了一种基于时延嵌入的动态系统重构方法,并将其用于轴承故障诊断。基于一个Wasserstein距离,即推土机距离(EMD),我们将重构的轴承振动数据与正常(健康)轴承数据的基线重构进行比较,以生成随时间变化的严重性指数。通过在多维标度(MDS)结果的二维图中可视化不同的重构动力系统之间的距离来执行故障类型评估。我们用两个实验室轴承退化数据集说明了该方法。我们将趋势与从时域和频域获得的统计特征以及EMD趋势进行了比较,而无需使用相空间重构。重建的EMD显示出早期断层与正常数据之间的差异很大,然后降低到正常方位。基于原始数据特征中不存在的形状相似性信息,建议将EMD比作为故障严重性指标。 MDS结果可以很好地可视化时间序列之间的距离,以进行聚类和故障类型诊断。

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