基于EMD和GA-BP的轴承智能诊断技术研究

Xing Zhikai, Qiang Wang, Yongbao Liu, Y. Guo, Jia Yan, Lihuan Mo, Jun Li
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引用次数: 0

摘要

轴承是旋转机械的核心部件,在发生故障时可能对设备产生重大影响。本文提出了一种基于EMD和GA-BP算法相结合的智能诊断技术,用于滚动轴承故障识别与分类问题。首先,采用EMD方法对测试数据进行处理,实现微故障的特征增强和提取,并将轴承作为经过遗传算法优化的bpnn的训练集和测试集进行故障排除。结果表明,与未利用能量特征法相比,该方法的精度和收敛速度均有提高,能够有效地进行轴承故障的识别与诊断。
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 Bearing Intelligent Diagnostic Technology Based on EMD and GA-BP
Bearings are the core components of rotating machinery and can have a significant impact on the equipment in the event of a failure. In this paper, an intelligent diagnostic technique based on the combination of EMD and GA-BP algorithm sifts with the rolling bearing fault identification and classification problem. First, the test data is processed by EMD method, the characteristic enhancement and extraction of micro-faults is realized, and the bearings are trouble shoot as training sets and test sets of BPNNs optimized by the built GA. The results show that the accuracy and convergence speed of this method are improved compared with the method of unutilized energy characteristics, and the identification and diagnosis of bearing fault can be effectively carried out.
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