Abstract

In the frame of this study, the problem of detecting the anomalies in nonstationary process signals as earlier signs of equipment faults and breakdowns is considered. The approach to the detection of anomalies by using the Hilbert–Huang transform in combination with the statistical model is presented. The main idea of this approach consists in analyzing the statistical parameters of the elements of Hilbert–Huang transform, which is adaptive in the case of nonstationary data and provides high itemization in the frequency-time region. The schematic layout and algorithm of this approach, the statistical classification model, the numerical calculations on model and real data, and the comparative analysis with other methods of detecting the anomalies in signals are described.

中文翻译：

---

使用希尔伯特-黄变换检测过程信号异常的方法

摘要

在本研究的框架中，考虑了检测非平稳过程信号中的异常作为设备故障和故障的早期迹象的问题。提出了利用希尔伯特-黄变换结合统计模型来检测异常的方法。该方法的主要思想在于分析 Hilbert-Huang 变换元素的统计参数，该变换在非平稳数据的情况下具有自适应性，并在频率-时间区域提供高分项。介绍了该方法的示意图和算法、统计分类模型、模型与真实数据的数值计算，以及与其他信号异常检测方法的对比分析。