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

TL;DR

This paper presents a hybrid machine learning approach combining clustering and dimensionality reduction to accurately diagnose faults and assign health scores to railcar fleets, improving maintenance decision-making.

Contribution

It introduces a novel hybrid fault diagnosis system using DBSCAN and PCA for railcar health assessment, enhancing predictive maintenance accuracy.

Findings

Detects 96.4% of failures within 50% of the sample

Effectively reduces data dimensionality and redundancy

Improves maintenance planning decisions

Abstract

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.