用简单规则预测地铁列车故障，仅靠三个传感器即可实现可解释预测。
Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset
- 基于在线规则的可解释方法，使用直观特征生成简洁判断逻辑。
- 仅需三个传感器数据，即能在MetroPT2数据集上准确预测故障。
- 适合需要透明决策的工业运维场景，如城市轨道交通系统。
近年来，深度学习在预测性维护中广泛应用，因其高预测性能。然而，在真实应用中，可解释性需求常被提及却未充分满足。本研究聚焦于葡萄牙波尔图地铁列车的故障预测问题。尽管已有研究提出高性能的深度神经网络架构并搭配并行可解释性流程，但生成的解释仍较复杂，难以说明故障原因。本文提出一种简单的在线规则化可解释方法，采用可理解的特征，生成清晰、易懂的判定规则。在MetroPT2数据集上的实验表明，仅需三个特定传感器的数据，即可通过简单规则准确预测该数据集中存在的故障。
原文摘要 · Abstract (English)
Due to their high predictive performance, predictive maintenance applications have increasingly been approached with Deep Learning techniques in recent years. However, as in other real-world application scenarios, the need for explainability is often stated but not sufficiently addressed. This study will focus on predicting failures on Metro trains in Porto, Portugal. While recent works have found high-performing deep neural network architectures that feature a parallel explainability pipeline, the generated explanations are fairly complicated and need help explaining why the failures are happening. This work proposes a simple online rule-based explainability approach with interpretable features that leads to straightforward, interpretable rules. We showcase our approach on MetroPT2 and find that three specific sensors on the Metro do Porto trains suffice to predict the failures present in the dataset with simple rules.
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。