用约束自编码器同时实现设备状态异常检测与退化趋势预测。
Constraint Guided AutoEncoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring
- 在自编码器中加入单调性约束，确保状态指标随时间递增。
- 异常检测性能与原模型相当，状态指标更符合实际退化规律。
- 适合工业设备健康监测场景，尤其关注长期退化趋势的用户。
工业设备状态监测的核心目标是区分正常与异常数据（即异常检测，AD），或预测反映资产全生命周期状态的条件指标（CI）。由于设备通常呈渐进式退化，理想情况下CI应随时间单调上升。本文提出对约束自编码器（CGAE）的扩展方法，使单一模型同时完成异常检测与具有单调性的CI估计。该方法引入时间序列单调性约束，强制模型输出的CI预测随时间递增。实验结果表明，新方法在异常检测性能上与原CGAE相当或略有提升，同时显著改善了CI的单调性表现。
原文摘要 · Abstract (English)
The main goal of machine condition monitoring is, as the name implies, to monitor the condition of industrial applications. The objective of this monitoring can be mainly split into two problems. A diagnostic problem, where normal data should be distinguished from anomalous data, otherwise called Anomaly Detection (AD), or a prognostic problem, where the aim is to predict the evolution of a Condition Indicator (CI) that reflects the condition of an asset throughout its life time. When considering machine condition monitoring, it is expected that this CI shows a monotonic behavior, as the condition of a machine gradually degrades over time. This work proposes an extension to Constraint Guided AutoEncoders (CGAE), which is a robust AD method, that enables building a single model that can be used for both AD and CI estimation. For the purpose of improved CI estimation the extension incorporates a constraint that enforces the model to have monotonically increasing CI predictions over time. Experimental results indicate that the proposed algorithm performs similar, or slightly better, than CGAE, with regards to AD, while improving the monotonic behavior of the CI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。