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基于HFMD和双分支并行网络在声信号下对滚动轴承的故障诊断

Hengdi Wang¹, Haokui Wang¹, Jizhan Xie¹

¹School of Mechanical and Electrical Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)

|September 13, 2025

概括

本研究介绍了一种使用优化特征模式分解 (HFMD) 和双分支并行网络的新型滚动轴承故障诊断方法. 这种方法显著提高了诊断准确性和稳定性,即使数据有限.

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:

滚动轴承故障诊断面临着数据质量差异和复杂故障条件的挑战.
传统方法在特征提取,噪声敏感性和多故障场景方面扎.

研究的目的:

开发一种先进的滚动轴承故障诊断方法,以解决因数据质量不同而导致的准确性限制.
在杂和复杂的故障条件下改善特征提取和分类性能.

主要方法:

优化特征模式分解 (HFMD) 使用HawkFish优化算法进行准确的模式分解.
通过剩余能源指数 (REI) 标准选择最佳的模式组件.
一个双分支并行网络,将Res2Net与ECA集成为时间特征,Swin变压器与MSCSA集成为时间频率特征.

主要成果:

在开源数据集上实现了100%的准确性.
与实验数据上的其他模型相比,证明了更高的诊断准确性 (98.5%).
在少数射击学习条件下保持高精度 (≥95%) 与最小变化 (2.34%).

结论:

拟议的HFMD和双分支并行网络方法为滚动轴承故障诊断提供了显著的稳定性和优越性.
这种方法有效地处理稀疏特征分离,并在各种数据质量场景中提高诊断准确性.
集成先进的信号处理和深度学习架构为复杂的机械健康监控提供了强大的解决方案.

关键词:

声学信号是一个声学信号.双分支并行网络双分支并行网络 "鱼优化算法"的优化算法错误诊断错误诊断错误诊断是一个问题.功能模式分解分解滚动轴承滚动轴承滚动轴承

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