一种智能故障诊断模型,用于在多条件和有限样本场景中具有自适应性超参数调的轴承
Jianqiao Li1, Zhihao Huang2, Liang Jiang3
1Faculty of Engineering, Monash University, Clayton, VIC, 3800, Australia.
Scientific reports
|March 25, 2025
概括
这项研究引入了一种混合灰狼算法 (HGWA) 优化的卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 来进行高级轴承故障诊断. 该方法实现了100%的准确性,即使在新的环境中数据有限.
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:
- 在不同的条件和有限的数据下,轴承故障诊断是复杂的.
- 传统方法与超参数调整和数据稀缺性作斗争.
研究的目的:
- 开发一种先进的轴承故障诊断方法,使用一种混合HGWA优化的CNN-BiLSTM架构.
- 为了提高诊断的准确性和效率,特别是在受限样本条件下.
主要方法:
- 使用CNN用于空间特征提取和BiLSTM用于时间依赖性捕获.
- 采用混合灰狼算法 (HGWA) 进行高效的神经网络超参数优化.
- 在多个操作条件中对CWRU数据集验证模型.
主要成果:
- 通过使用CWRU数据集,在四个操作条件中实现了100%的诊断准确性.
- 作为预训练模型,在新环境中使用最小的数据证明了高诊断准确性.
- 展示了增强的优化效率和更快的结果.
结论:
- 拟议的HGWA优化CNN-BiLSTM模型有效地解决了在受限样本下进行故障诊断的挑战.
- 这种方法为现实世界滚动轴承故障诊断提供了强大而高效的解决方案.
- 该方法有助于显著提高诊断性能和优化效率.
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