Developing Hybrid Machine Learning Models to Assign Health Score to Railcar Fleets for Optimal Decision Making Mahyar Ejlali, Ebrahim Arian, Sajjad Taghiyeh, Kristina Chambers, Amir Hossein Sadeghi, Demet Cakdi, Robert B Handfield An expert hybrid predictive fault method is proposed based on fast-DBSCAN and PCA. Inspection data from 1986-2020 of North American Railcar Owner (NARO) is used. The model is able to predict future faults in the railcar fleet accurately. 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. Keywords: Expert system, Predictive maintenance, Railcar maintenance, Machine learning, Maintenance health score 1. Introduction Maintenance consists of activities that ensure the railcar assets continue to operate safely and reliably. These activities include inspection, repair, testing, and replacement of parts.

Auto dataset available in R, ISLR package was used for this analysis. Purpose of ML model Predict the car mileage per gallon based on features like weight and year of manufacture. KNN (K-Nearest Neighbor) regression model is being used. Process for Creating ML Model - Divide dataset into train and test sets, 65% and 35% approximately. This resulted in a MSE of 15.25 - Applied KNN regression for 50 k-values and computed mean squared error.