一个融合模型用于智能诊断小样本尺寸的轮故障
Jianing Huang1, Zikang Liu2, Jianggui Han1
1College of Power Engineering, Naval University of Engineering, Wuhan 430072, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究引入了一种新的CBAM-TCN-SVM模型,用于智能轮故障诊断. 融合方法实现了98.3%的准确性,克服了数据限制,并依赖于先前的知识来有效地预测轮状况.
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:
- 轮故障是旋转机械故障的常见原因之一.
- 当前的智能诊断模型面临着数据要求和特征提取方面的挑战.
- 浅层模型需要手动功能工程,而深层模型需要广泛的数据.
研究的目的:
- 提出一种新的融合模型,CBAM-TCN-SVM,用于智能轮故障诊断.
- 解决现有模型关于数据规模和先前知识依赖性的局限性.
- 为了提高轮故障预测的准确性和可行性.
主要方法:
- 开发了一个融合模型,结合了时间卷积网络 (TCN),卷积块注意模块 (CBAM) 和支持向量机器 (SVM).
- 利用CBAM-TCN从使用注意力机制的频域序列数据中提取深故障特征.
- 采用SVM来最终智能分类轮故障类型.
主要成果:
- 拟议的CBAM-TCN-SVM模型实现了98.3%的分类准确性.
- 通过多层卷积和注意力机制证明了有效的深层断层特征提取.
- 验证了该模型在轮故障预测方面的可行性和卓越性能.
结论:
- CBAM-TCN-SVM融合模型成功地整合了深度学习和浅层分类的优势.
- 该方法克服了有限数据的限制,以及在轮故障诊断中依赖先前知识的限制.
- 这种方法为智能轮健康监测和预测提供了强大而准确的解决方案.
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