开发混合机器学习模型为铁路车队分配健康评分以实现最佳决策
Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making
摘要：
在铁路车队的运行过程中会产生大量的数据，这很容易导致维度灾难并降低铁路车辆网络的弹性。为了解决这些问题，并提供预测性维护，本研究介绍了一种混合故障诊断专家系统方法，结合基于密度的空间聚类的应用程序与噪声（DBSCAN）和主成分分析（PCA）。首先，使用DBSCAN方法对同一组中彼此相似的分类数据进行聚类。其次，应用主成分分析算法对数据进行降维，消除冗余，提高故障诊断的准确率。最后，我们解释了工程特性，并通过使用增益图和曲线下面积（AUC）指标来评估所选模型。我们使用的混合专家系统模型，以提高维修规划决策，通过分配一个健康评分的轨道车系统的北美铁路车业主（NARO）。根据实验结果，我们的专家模型可以检测到96.4%的故障在50%的样本。这表明，我们的方法是有效的，在诊断故障的铁路车队。
英文摘要：
A large amount of data is generated during the operation of a railcar fleet, which can easily lead to dimensional disaster and reduce the resiliency of the railcar network. To solve these issues and offer predictive maintenance, this research introduces a hybrid fault diagnosis expert system method that combines density-based spatial clustering of applications with noise (DBSCAN) and principal component analysis (PCA). Firstly, the DBSCAN method is used to cluster categorical data that are similar to one another within the same group. Secondly, PCA algorithm is applied to reduce the dimensionality of the data and eliminate redundancy in order to improve the accuracy of fault diagnosis. Finally, we explain the engineered features and evaluate the selected models by using the Gain Chart and Area Under Curve (AUC) metrics. We use the hybrid expert system model to enhance maintenance planning decisions by assigning a health score to the railcar system of the North American Railcar Owner (NARO). According to the experimental results, our expert model can detect 96.4% of failures within 50% of the sample. This suggests that our method is effective at diagnosing failures in railcars fleet.
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