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Shape classification of wear particles by image boundary analysis using machine learning algorithms

机译：使用机器学习算法通过图像边界分析对磨损颗粒的形状进行分类

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摘要

The shape features of wear particles generated from wear track usually contain plenty of information about the wear states of a machinery operational condition. Techniques to quickly identify types of wear particles quickly to respond to the machine operation and prolong the machine's life appear to be lacking and are yet to be established. To bridge rapid off-line feature recognition with on-line wear mode identification, this paper presents a new radial concave deviation (RCD) method that mainly involves the use of the particle boundary signal to analyze wear particle features. Signal output from the RCDs subsequently facilitates the determination of several other feature parameters, typically relevant to the shape and size of the wear particle. Debris feature and type are identified through the use of various classification methods, such as linear discriminant analysis, quadratic discriminant analysis, naieve Bayesian method, and classification and regression tree method (CART). The average errors of the training and test via ten-fold cross validation suggest CART is a highly suitable approach for classifying and analyzing particle features. Furthermore, the results of the wear debris analysis enable the maintenance team to diagnose faults appropriately.
机译：由磨损轨迹产生的磨损颗粒的形状特征通常包含有关机械运行条件的磨损状态的大量信息。快速识别磨损颗粒类型以响应机器操作并延长机器寿命的技术似乎缺乏，并且尚未建立。为了将快速离线特征识别与在线磨损模式识别联系起来，本文提出了一种新的径向凹面偏差（RCD）方法，该方法主要涉及使用粒子边界信号来分析磨损粒子特征。随后，从RCD输出的信号有助于确定其他几个特征参数，这些参数通常与磨损颗粒的形状和大小有关。碎片的特征和类型可通过使用各种分类方法来识别，例如线性判别分析，二次判别分析，朴素贝叶斯方法以及分类和回归树方法（CART）。通过十倍交叉验证进行的训练和测试的平均误差表明，CART是用于分类和分析粒子特征的高度适合的方法。此外，磨损碎片分析的结果使维护团队能够适当地诊断故障。

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  • 来源
    《Mechanical systems and signal processing》 |2016年第5期|346-358|共13页
  • 作者

    Wei Yuan; K.S. Chin; Meng Hua; Guangneng Dong; Chunhui Wang;

  • 作者单位

    Key Laboratory of Modern Design and Rotor-Bearing System of the Education Ministry, Xi'an Jiaotong University, Xi'an 710049, China,Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China;

    Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China;

    Department of Mechanical and Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China;

    Key Laboratory of Modern Design and Rotor-Bearing System of the Education Ministry, Xi'an Jiaotong University, Xi'an 710049, China;

    Key Laboratory of Modern Design and Rotor-Bearing System of the Education Ministry, Xi'an Jiaotong University, Xi'an 710049, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wear particles; Image processing; Radial concave deviation; Particle classification; Machine learning;

    机译：磨损颗粒;图像处理;径向凹偏差;颗粒分类;机器学习;

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