基于马尔科夫过渡场和SE-ResNet的行星变速箱故障诊断
Yanyan Liu1, Tongxin Gao1, Wenxu Wu2
1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种新的故障诊断方法,用于使用马尔科夫过渡场和残留注意力机制的行星变速箱. 这种方法有效地识别了变速箱故障的高精度,即使在噪音条件下.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 由于复杂的工作条件和强大的结构合,行星变速箱面临可靠性问题.
- 传统的深度神经网络在杂的环境和一维数据中难以进行特征学习,限制了故障诊断.
- 现有的方法往往无法捕捉数据点内的相互关系,从而影响诊断的准确性.
研究的目的:
- 为行星变速箱开发一种先进的故障诊断方法.
- 在杂和复杂的环境中克服传统深度学习方法的局限性.
- 为了提高行星变速箱故障检测的可靠性和准确性.
主要方法:
- 整合马尔科夫过渡场 (MTF) 来编码1D信号到特征图中.
- 使用剩余网络 (ResNet) 架构进行特征提取.
- 在ResNet34 (SE-ResNet) 中嵌入一个挤压和激发 (SE) 通道注意力机制,以改善功能聚焦.
主要成果:
- 拟议的SE-ResNet模型有效地从行星变速箱数据中提取和分类特征.
- 该方法在强噪声条件下诊断故障方面表现出很高的性能.
- 对特定数据集的验证产生了大约98.1%的令人印象深刻的准确性.
结论:
- 集成MTF和SE-ResNet开发的故障诊断方法是有效和可靠的.
- 这种方法显著提高了在具有挑战性的环境中行星变速箱故障检测的准确性.
- 这项研究强调了基于注意力的深度学习对于强大的状态监测的潜力.
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