域适应网络,具有无参数的适应性纠正线性单位,用于在可变操作条件下进行故障诊断
IEEE transactions on neural networks and learning systems
|August 7, 2023
概括
本研究引入了一种新的无参数激活功能 (PfAReLU),用于在旋转机械故障诊断中进行深度学习. 它通过适应可变条件并减少过风险来改善跨领域诊断.
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:
- 旋转机械中的滚动轴承面临着变速/负载条件的变化.
- 振动信号与操作条件有很大差异,使故障诊断复杂化.
- 现有的深度学习方法在跨领域故障诊断中与固定的转换作斗争.
研究的目的:
- 提出一个新的激活功能,无参数自适应纠正线性单位 (PfAReLU),以改善跨域故障诊断.
- 与PfAReLU (DPRCN-PfAReLU) 共同开发深度无参数重建分类网络.
- 提高深度学习模型在不同操作条件下捕获故障特征的能力.
主要方法:
- 引入了PfAReLU,一种具有自适应非线性转换和零参数的激活函数.
- 开发了DPRCN-PfAReLU,具有共享编码器与无参数注意模块,目标域解码器和源域分类器.
- 利用振动信号分析进行跨域故障诊断.
主要成果:
- PfAReLU展示了根据输入数据量身定制的适应性非线性转换.
- 由于PfAReLU的零参数性质降低了网络过度装配的风险.
- 在没有额外的参数的情况下,DPRCN-PfAReLU有效地增强了域不变特征.
- 拟议的方法在实验中在9个不同的操作条件下在跨领域故障诊断中表现出卓越的性能.
结论:
- PfAReLU是一种新且有效的激活功能,用于滚动轴承的跨域故障诊断.
- DPRCN-PfAReLU提供了一个强大的解决方案,用于在各种操作条件下诊断故障.
- 无参数方法显著提高了诊断准确性,并减少了模型的复杂性.
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