多源域区分网络用于在未见条件下进行工业智能监控
IEEE transactions on cybernetics
|October 28, 2025
概括
本研究引入了一种新的多源域区分网络 (MS-DSN),以提高工业时间序列预测的准确性. 该方法提高了对不断变化的工业数据分布的模型稳定性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 工业监控 工业监控 工业监控
背景情况:
- 工业时间序列预测对于智能监控至关重要,但由于运营条件的变化而受到分配转移的影响.
- 现有的域泛化方法在回归任务中经常失败,或者通过掩盖相关信息来降低预测准确性.
- 当前的域泛化技术可能对极端数据分布敏感,影响模型的稳定性.
研究的目的:
- 为工业时间序列回归任务开发一个强大的域泛化框架.
- 解决现有方法的局限性,包括缺少回归支持,信息丢失和对极端数据的敏感性.
- 提高工业监测模型的准确性和泛化能力.
主要方法:
- 建议建立一个多源域区分网络 (MS-DSN),将特征划分为域私有和共享空间.
- 定义了一个监督的对比损失,以保留标签相关的信息在域共享空间.
- 使用定向风险推算 (DREx) 方法处理极其分布式的数据.
主要成果:
- 在MS-DSN有效地过域特定的信息,同时保持域不变的特征.
- 拟议的方法通过保留与任务相关的信息来保持预测精度.
- 在CMAPSS和N-CMAPSS数据集上的实验证明了MS-DSN的有效性.
结论:
- MS-DSN 提供了一个可靠的解决方案,用于在分配转移下进行工业时间序列预测.
- 该方法在动态的工业环境中增强了模型的概括性和准确性.
- 提出的方法克服了现有的回归任务域泛化技术的局限性.
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