基于WSST和ISSA-MCNN-BIGRU的轴承故障诊断方法
Shien Dong1, Weiyan Tong2, Hongwei Bai1
1School of Chemical Process Automation, Shenyang University of Technology, Liaoyang, 111003, China.
Scientific reports
|November 25, 2025
概括
这项研究引入了用于滚动轴承故障诊断的先进混合框架,通过整合波形同步压缩转换 (WSST) 和优化神经网络的改进搜索算法 (ISSA) 来实现99.75%的准确性.
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:
- 滚动轴承在旋转机械中至关重要,但它们的故障诊断在特征提取和准确性方面面临挑战.
- 现有的方法往往严重依赖专家的经验,限制了自动诊断能力.
研究的目的:
- 为滚动轴承开发一个强大而准确的故障诊断框架.
- 克服当前诊断方法的特征提取和识别率的局限性.
主要方法:
- 这是一个混合框架,它结合了波形同步压缩转换 (WSST) 用于信号表示,多尺度卷积神经网络 (MCNN) 用于空间特征提取,以及双向门循环单元 (BiGRU) 用于时间依赖性学习.
- 一个改进的乌搜索算法 (ISSA),结合混乱的帐映射,高斯基变异和利维飞行,用于MCNN-BiGRU网络的自适应性超参数优化.
- 该框架使用来自Case Western Reserve大学和东南大学的轴承数据集进行了验证.
主要成果:
- 拟议的ISSA-MCNN-BiGRU模型实现了最大的故障诊断准确率为99.75%.
- 与基线模型 (GRU,BiGRU,MCNN-BiGRU,PSO-MCNN-BiGRU,GA-MCNN-BiGRU) 相比,该模型在准确性,稳定性和概括性方面表现出更好的表现.
- 该框架在各种噪音环境中表现出强大的稳定性和显著更高的准确性.
结论:
- 开发的混合诊断框架为滚动轴承故障诊断提供了一个高度准确和强大的解决方案.
- 集成WSST,MCNN,BiGRU和ISSA有效地解决了特征提取和诊断准确性的挑战.
- 这种方法显示出提高大型旋转机械的可靠性和安全性的巨大潜力.
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