在设备异常检测复杂缺失模式下的表达增强图形时间卷积网络下
Liangmei Luo1, Zhixuan Li2, Shuying Wang1
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611731, China.
ISA transactions
|January 23, 2026
概括
这项研究引入了一种新的方法,用于检测设备中的异常,使用多变量时间序列数据与缺失值. 开发的方法增强了数据表示,以提高异常检测系统的可靠性.
科学领域:
- 工业物联网工业物联网工业物联网
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 设备的多变量时间序列异常检测对于操作可靠性至关重要.
- 现有的方法在缺少数据方面扎,从而影响异常检测的准确性.
- 工业设备中复杂的缺失数据模式带来了重大挑战.
研究的目的:
- 提出一种用于在复杂的缺失数据模式下检测设备异常的新方法.
- 通过整合重建和预测来增强系统健康状况的表示.
- 在缺少数据的情况下提高异常检测的可靠性和准确性.
主要方法:
- 开发了一个代表性增强的图形时间卷积网络 (REGTCN).
- 基于重建和基于预测的综合范式,用于联合优化.
- 使用一个缺失耐受性掩盖图表注意力 (MGAT) 网络进行重建.
- 采用适应性的多尺度时间卷积相互作用网络 (AMTCIN) 进行预测.
主要成果:
- 拟议的 REGTCN 方法有效处理复杂的缺失数据模式.
- 实验结果显示,在各种缺失数据场景中,与基线模型相比,性能优越.
- 综合框架增强了系统健康状况的表现.
结论:
- 该REGTCN方法提供了一个强大的解决方案,用于多变量时间序列异常检测缺失的数据.
- 这种方法显著提高了工业设备异常检测的可靠性.
- 该研究强调了解决时间序列分析中缺失数据挑战的重要性.
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