概括
本综述详细介绍了故障诊断的增量学习,使工业系统能够持续更新模型. 它解决了诸如概念漂移和灾难性遗忘在不断发展的故障检测中的挑战.
科学领域:
- 工业工程 工业工程 工业工程
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 有效的故障诊断对于工业系统的可靠性和安全性至关重要.
- 工业系统中的非静止数据流需要适应性诊断模型.
- 现有的审查没有特别关注用于故障诊断的增量学习.
研究的目的:
- 系统地审查最先进的增量学习支持故障诊断方法.
- 在故障诊断中专注于增量学习策略.
- 为这个不断发展的领域呈现一种新的分类学和视角.
主要方法:
- 系统审查增量学习支持的故障诊断文献.
- 基于增量学习策略和应用背景的方法的分类.
- 讨论在故障诊断中增量学习的挑战和新出现的解决方案.
主要成果:
- 识别和分类目前用于故障诊断的增量学习方法.
- 分析关键挑战,包括概念漂移和灾难性遗忘.
- 概述了为减轻这些挑战提出的解决方案.
结论:
- 这项工作提供了第一个全面的回顾,专门针对故障诊断的增量学习.
- 它为研究人员和从业人员提供了一个结构化的概述和新的分类学.
- 它强调了增量学习的潜力,以提高工业环境中的自适应故障检测.
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