一种基于多传感器融合技术和EB-1D-TP编码算法的滚动轴承新型故障分类特征提取方法
Zuozhou Pan1, Zhengyuan Zhang2, Zong Meng3
1College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou 310018, PR China.
ISA transactions
|August 12, 2023
概括
这项研究引入了一种使用多传感器融合和增强的二元一维三元模式 (EB-1D-TP) 算法进行轴承故障诊断的新方法. 这种方法显著提高了分类滚动轴承故障的准确性和速度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 在多传感器监控环境中,准确的轴承故障诊断至关重要.
- 现有的方法往往难以提取有效的故障分类特征.
- 需要增强特征提取,以提高诊断准确度.
研究的目的:
- 提出一种新的轴承故障分类特征提取方法.
- 为了提高滚动轴承故障诊断的准确性和速度.
- 为了利用多传感器融合技术和增强的二进制一维三进制模式 (EB-1D-TP) 算法.
主要方法:
- 为高精度信号融合开发了一个最佳的等分权重算法.
- 引入了一种增强的二进制编码方法 (类似于平衡的三进制编码),以增加特征差异化.
- 使用支持矢量机 (SVM) 用于使用合和编码特征进行故障分类.
主要成果:
- 拟议的算法显著提高了滚动轴承故障分类的准确性.
- 该方法显示,故障分类的速度显著增加.
- 将融合编码功能与其他智能分类器相结合,进一步增强了诊断结果.
结论:
- 多传感器融合和EB-1D-TP算法为轴承故障诊断提供了一个强大的工具.
- 增强的特征提取方法导致更准确,更有效的故障识别.
- 这种方法为改善状态监测系统提供了一个有希望的方向.
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