TRA-ACGAN:基于辅助分类器生成对抗网络和变压器网络的电机轴承故障诊断模型
Zhaoyang Fu1, Zheng Liu2, Shuangrui Ping1
1School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
ISA transactions
|April 11, 2024
概括
这项研究引入了一种新的变压器和辅助分类器生成对抗网络 (TRA-ACGAN) 用于电机轴承故障诊断. TRA-ACGAN有效地扩展了有限的故障数据,提高了诊断准确性和效率.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 电机轴承故障诊断对于工业效率和安全至关重要.
- 深度学习方法是有效的,但往往因为缺少故障数据而扎.
- 生成对抗网络 (GAN) 显示出在故障诊断中数据增强的前景.
研究的目的:
- 为了应对电机轴承故障诊断中有限的故障数据的挑战.
- 提出一种新的深度学习模型,用于增强故障诊断.
- 提高电机轴承故障检测系统的准确性和可靠性.
主要方法:
- 辅助分类器生成对抗网络 (ACGAN) 的应用,用于数据扩展.
- 开发了一个新的变压器和ACGAN (TRA-ACGAN) 模型.
- 利用注意力机制来有效地提取特征,避免双重任务合问题.
主要成果:
- 拟议的TRA-ACGAN模型有效地扩展了小型故障数据集.
- 在CWRU和PU数据集上的实验验证表明了卓越的性能.
- 该模型显示了与传统方法相比的适用性和优越性.
结论:
- 在有限的数据的情况下,TRA-ACGAN模型为电机轴承故障诊断提供了强大的解决方案.
- 变压器网络的集成增强了特征提取和诊断精度.
- 这种方法在旋转机械的智能故障诊断领域取得了重大进展.
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