用可解释的NARX模型分析铁路传感器数据，辅助安全维护决策
Insightful Railway Track Evaluation: Leveraging NARX Feature Interpretation
- 结合NARX与逻辑回归，实现多分类可解释建模
- 通过特征重要性分析，揭示关键故障指标
- 适合铁路运维人员做故障预警与决策支持
时间序列分类对工程领域提取有意义洞察和辅助决策至关重要。参数化建模方法如NARX因其结构透明、易于解释，特别适用于环境时间序列等复杂过程的理解。本文提出一种名为Logistic-NARX Multinomial的分类算法，将NARX方法与逻辑回归相结合，不仅生成可解释模型，还能有效应对多分类挑战。此外，本研究针对铁路领域提出创新方法，利用NARX模型解析车载传感器产生的大量特征，通过特征重要性分析提供深刻洞见，支持安全与维护方面的科学决策。
原文摘要 · Abstract (English)
The classification of time series is essential for extracting meaningful insights and aiding decision-making in engineering domains. Parametric modeling techniques like NARX are invaluable for comprehending intricate processes, such as environmental time series, owing to their easily interpretable and transparent structures. This article introduces a classification algorithm, Logistic-NARX Multinomial, which merges the NARX methodology with logistic regression. This approach not only produces interpretable models but also effectively tackles challenges associated with multiclass classification. Furthermore, this study introduces an innovative methodology tailored for the railway sector, offering a tool by employing NARX models to interpret the multitude of features derived from onboard sensors. This solution provides profound insights through feature importance analysis, enabling informed decision-making regarding safety and maintenance.
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