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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 therailcar network. To solve these issues and offer predictive maintenance, thisresearch introduces a hybrid fault diagnosis expert system method that combinesdensity-based spatial clustering of applications with noise (DBSCAN) andprincipal component analysis (PCA). Firstly, the DBSCAN method is used tocluster categorical data that are similar to one another within the same group.Secondly, PCA algorithm is applied to reduce the dimensionality of the data andeliminate redundancy in order to improve the accuracy of fault diagnosis.Finally, we explain the engineered features and evaluate the selected models byusing the Gain Chart and Area Under Curve (AUC) metrics. We use the hybridexpert system model to enhance maintenance planning decisions by assigning ahealth score to the railcar system of the North American Railcar Owner (NARO).According to the experimental results, our expert model can detect 96.4% offailures within 50% of the sample. This suggests that our method is effectiveat diagnosing failures in railcars fleet.

Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making