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Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

机译：基于黎曼流形的风力发电机齿轮箱故障诊断方法

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摘要

As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.
机译：随着社会生产和生活中广泛存在多种时间序列问题，故障诊断方法为人们提供了金融，水文，气象，地震，视频监控，医学等领域的大量有价值的信息。为了快速有效地按时间顺序查找故障，提出了一种基于黎曼流形的多元时间序列处理方法。该方法基于滑动窗口，并使用协方差矩阵作为时间序列的描述符。将黎曼距离用作相似性度量，并将统计过程控制图应用于检测多元时间序列的异常。利用协方差矩阵分布的可视化检测机械设备的异常情况，从而实现故障诊断。以风力发电机齿轮箱故障为实验对象，对故障诊断方法进行了验证，结果表明该方法是合理有效的。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第8期|153656.1-153656.10|共10页
  • 作者

    Shoubin Wang; Xiaogang Sun; Chengwei Li;

  • 作者单位

    School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China;

    School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China;

    School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China;

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