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国外文献 > 1.轴承 > 6. 信号处理与特征分析 > 特征与分类

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标 题： A Method of Feature Extraction from Time-Frequency Images of Vibration Signals in Faulty Bearings for Classification Purposes
用于故障轴承振动信号时频图像特征提取的方法及其分类应用
作 者： Dorel Aiordachioaie
Theodor D. Popescu
Bogdan Dumitrascu
关键词： Signal  [信号]
Vibration  [ 振动]
Image  [ 图片]
Time-frequency transform  [ 时频变换]
Signal processing  [ 信号处理]
Feature selection  [ 特征选择]
Classification  [ 分类]
出版年： 2019
会议名： 11th International Conference on Emerging Networks and Systems Intelligence (EMERGING 2019)
第11届新兴网络与系统智能国际会议（EMERGING 2019）
届 次： 11th
摘 要： Time-frequency image processing is considered in the context of change detection and diagnosis purposes of bearings with faults and vibration signal processing. The analysis of these images reveals some difficulties in obtaining accurate results in classification. Some images are different compared to other images from the same set. New images are obtained by applying a criterion based on the contours generated by the main components of the analyzed time-frequency image. The transformed images are less complex, and could be with white and black only. Features based on statistical moments are considered, selected and used to define discriminant functions, which could improve the results of the classification. The features include the number of the contours, the average area defined by the contours, the variance of the areas and the Renyi entropies.

在故障轴承的异常检测与诊断以及振动信号处理中，考虑了时频图像处理。对这些图像进行分析后发现，在分类过程中难以获得准确结果。某些图像与其他同一批图像存在差异。通过基于所分析的时频图像主成分生成的轮廓来设定判据，从而获取新的图像。变换后的图像结构更简单，可能仅包含黑白两色。采用基于统计矩的特征进行分析、选择并用于定义判别函数，以提升分类效果。这些特征包括轮廓数量、由轮廓定义的平均面积、面积的方差以及Renyi熵。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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