基于Duffing振荡器的轴承故障微弱信号检测研究

Hao Long, Dan Liu, Fei Liu, Qingxin Wang, Lin Liang, Guanghua Xu
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引用次数: 1

摘要

本文将混沌系统应用于轴承早期故障的微弱信号识别和提取,这些信号通常被淹没在强背景噪声中。混沌系统具有抗噪声和对弱周期信号的敏感性,是一种有效的微弱信号检测方法。然而,混沌系统在临界混沌状态下并非完全不受噪声的影响。针对这一问题,采用四个指标来评价Duffing振荡器的检测性能。然后,详细研究了Duffing振荡器参数对四项指标的影响,提出了一种提高Duffing振荡器检测性能的新方法。仿真和实验结果表明,该方法能在较低信噪比的情况下准确获取轴承早期故障的特征信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on the Weak Signal Detection of Bearing Fault Based on Duffing Oscillator
In this paper, chaotic system is applied to identify and extract the weak signals of bearing early fault which are often submerged in strong background noise. Chaotic system is an effective method in weak signal detection because of its properties of noise immunity and sensitivity to the weak periodic signal. However, chaotic system is not completely immune to noise in critical chaotic state. Aiming at this problem, four indicators are used to evaluate the detection performance of Duffing oscillators. Then, the influence of Duffing oscillator parameters on the four indicators is studied in detail and a new method is proposed to improve the detection performance of Duffing oscillator. The simulation and experimental results show that the proposed method can accurately obtain the characteristic signals of early bearing fault in a lower signal-to-noise ratio (SNR) situation.
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