基于时频域分析和EMD的滚动轴承故障诊断

Liandie Zhu, W. Dai, Guixiu Luo, Rui Du
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引用次数: 0

摘要

预测与健康管理(PHM)技术是利用大量的状态监测数据和信息,借助各种故障模型和人工智能算法对设备的健康状态进行监测、诊断、预测和管理的技术,通过预测问题和可靠的工作寿命,提高设备的安全性,最大限度地减少故障的影响,本文在滚动轴承、利用Labview软件构建时域分析程序,从不同角度(35Hz12KN/37.5Hz11KN/40HZ10KN)对滚动轴承进行三种工况分析,最后利用Matlab软件进行频域分析和经验模态分解(EMD),提取固有模态函数和振动信号频谱,找出故障特征频带,为轴承在不同载荷下的故障诊断提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault diagnosis of rolling bearing based on time and frequency domain analysis and EMD
Prognostic and health management (PHM) technology is the use of a large amount of condition monitoring data and information, with the help of all kinds of fault model and artificial intelligence algorithms monitoring, diagnosis, prediction and management of the health status of the equipment technology, by predicting the problems and reliable working life, improving the safety of equipment, minimizing the fault effect, this article in rolling bearing, using Labview software construction time domain analysis program, the analysis of three kinds of condition from different perspective (35Hz12KN/37.5Hz11KN/40HZ10KN)under the rolling bearing, Finally, Matlab software was used for frequency domain analysis and empirical mode decomposition (EMD), and the inherent modal function and vibration signal spectrum were extracted to find out the fault characteristic frequency band, which provided a basis for bearing fault diagnosis under different loads.
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