转移学习激发智能故障诊断设计:一项调查,见解和观点
概括
转移学习增强了自动化中的故障诊断 (FD),使系统能够适应不断变化的条件. 本综述详细介绍了转移学习驱动的FD方法,以提高安全性和可靠性.
科学领域:
- 自动化系统工程 自动化系统工程
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 现代自动化系统需要强大的故障诊断 (FD),以确保安全和可靠性.
- 环境变化和组件退化等挑战需要适应性FD方法.
- 转移学习为开发具有长期适用性的FD方法提供了一个范式.
研究的目的:
- 为转移学习驱动的故障诊断方法提供全面的审查.
- 专注于转移学习在FD任务中的具体应用.
- 为这些技术提出新的原则和分类策略.
主要方法:
- 对现有关于转移学习用于故障诊断的文献进行系统审查.
- 将方法分为两个子类:知识校准和知识妥协.
- 分析如何在FD任务中使用先前知识.
主要成果:
- 识别转移学习作为自我学习和自适应性FD的关键推动者.
- 介绍了在FD应用转移学习的三个指导原则.
- 引入一种新的分类策略,用于转移学习动机的FD技术.
结论:
- 转移学习对于开发弹性和适应性故障诊断系统至关重要.
- 提出的综述和分类提供了对该领域的结构化理解.
- 突出了FD转移学习的开放问题和未来的研究方向.
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