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根据传感器概念漂移的核电站运行状态的自适应预测,使用桥梁分布自适应网络
Kui Xu1, Linyu Liu1, Yang Lan1
1China Nuclear Power Operation Technology Corporation, Wuhan 430233, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究介绍了一种新的桥接分布适应网络 (BDAN),以解决核电站传感器数据中的概念漂移问题. BDAN有效地管理分布变化,大大提高了故障预测的准确性.
科学领域:
- 核工程 核工程是指核工程.
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 核电站的运行依赖于广泛的传感器网络以确保安全.
- 环境和操作变化可能会导致传感器数据的概念漂移,影响模型性能.
- 概念漂移,即随着时间的推移数据分布的变化,妨碍了准确的预测.
研究的目的:
- 开发一种处理核电站传感器数据中的非渐进概念漂移的方法.
- 设计一种神经网络架构,能够适应不断变化的数据分布.
- 为了提高核电运营中的故障预测准确度.
主要方法:
- 设计了一个桥接分布自适应网络 (BDAN),具有双同型神经网络和潜在的自适应桥接模块.
- 生成过渡中间分布以引导模型通过非渐进的变化.
- 利用到达时间差异 (TDoA) 的同型骨干来处理传感器数据.
- 计算了跨时间阶段的分布差异,以生成桥梁分布和更新模型参数.
主要成果:
- 与现有方法相比,BDAN表现出优越的性能.
- 实现了核能数据预测的平均平方误差减少5.6%.
- 成功适应时间域的非渐进分布变化.
- 展示了增强的故障预测能力.
结论:
- BDAN有效地减轻了核电站数据概念漂移的负面影响.
- 拟议的方法为动态环境中的实时故障预测提供了强大的解决方案.
- BDAN代表了应用人工智能在核安全和运营效率方面的重大进步.
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