错误诊断中的对抗性深度转移学习:进展,挑战和未来前景
Yu Guo1, Jundong Zhang1, Bin Sun1
1College of Marine Engineering, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
敌对深度转移学习 (ADTL) 通过改进特征表示和知识转移来增强智能故障诊断. 本综述对ADTL模型进行了分类,并讨论了工业应用的挑战.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 工业自动化 工业自动化
背景情况:
- 深度转移学习 (DTL) 集成了深度学习和转移学习,用于高级特征表示和知识转移.
- 在智能故障诊断 (IFD) 中,DTL显示出前景,但在复杂领域面临挑战.
- 对抗性深度转移学习 (ADTL) 已经出现,以解决这些局限性.
研究的目的:
- 审查和分类对抗性深度转移学习 (ADTL) 模型.
- 检查ADTL在智能故障诊断 (IFD) 领域的最新进展.
- 确定当前的挑战和ADTL在故障诊断中的未来方向.
主要方法:
- 将ADTL分为非生成模型和生成模型.
- 对ADTL进步的审查,重点是使用生成对抗网络 (GAN) 进行特征转移,映射关系和特征转换.
- 在智能故障诊断 (IFD) 中分析ADTL应用.
主要成果:
- 非生成的ADTL模型专注于高效的特征转移和映射.
- 生成型ADTL模型利用像GAN这样的技术来进行特征转换.
- 最近的进展显示了ADTL在改善IFD方面的潜力.
结论:
- ADTL为智能故障诊断 (IFD) 提供了重要的潜力.
- 关键的挑战包括数据不平衡,负转移和对抗训练稳定性.
- 未来的研究应该专注于优化ADTL用于现实世界的工业故障诊断.
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