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Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing

机译:基于声发射的状态监测方法:低速回转支承的回顾与应用

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摘要

This paper presents an acoustic emission-based method for the condition monitoring of low speed reversible slew bearings. Several acoustic emission (AE) hit parameters as the monitoring parameters for the detection of impending failure of slew bearings are reviewed first. The review focuses on: (1) the application of AE in typical rolling element bearings running at different speed classifications, i.e. high speed ( > 600 rpm), low speed (10-600 rpm) and very low speed (< 10 rpm); (2) the commonly used AE hit parameters in rolling element bearings and (3) AE signal processing, feature extraction and pattern recognition methods. In the experiment, impending failure of the slew bearing was detected by the AE hit parameters after the new bearing had run continuously for approximately 15 months. The slew bearing was then dismantled and the evidence of the early defect was analysed. Based on the result, we propose a feature extraction method of the AE waveform signal using the largest Lyapunov exponent (LLE) algorithm and demonstrate that the LLE feature can detect the sign of failure earlier than the AE hit parameters with improved prediction of the progressive trend of the defect.
机译:本文提出了一种基于声发射的低速可逆回转轴承状态监测方法。首先回顾了几个声发射(AE)命中参数,作为监测回转轴承即将失效的监测参数。审查的重点是:(1)AE在以不同速度等级运行的典型滚动轴承中的应用,即高速(> 600 rpm),低速(10-600 rpm)和极低速(<10 rpm); (2)滚动轴承中常用的AE命中参数,以及(3)AE信号处理,特征提取和模式识别方法。在实验中,新轴承连续运行约15个月后,通过AE碰撞参数检测到回转轴承即将发生故障。然后拆除旋转轴承,并分析早期缺陷的证据。根据结果​​,我们提出了一种使用最大Lyapunov指数(LLE)算法的AE波形信号特征提取方法,并证明了LLE特征可以比AE命中参数更早地检测出故障迹象,并改进了对渐进趋势的预测缺陷。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2016年第5期|134-159|共26页
  • 作者单位

    School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia,Mechanical Engineering Department, Diponegoro University, Tembalang, Semarang 50275, Indonesia;

    School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia;

    School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia;

    School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia;

    School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia;

    School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, Wollongong, New South Wales 2522, Australia;

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

    Acoustic emission; Condition monitoring; Feature extraction; Largest Lyapunov exponent (LLE) algorithm; Low speed slew bearing;

    机译:声发射;状态监测;特征提取;最大的Lyapunov指数(LLE)算法;低速回转轴承;

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