A three-step agglomerated Machine Learning: An alternative to Weibull Defect Analysis of Rail Infrastructure

Abstract

Rail defects constitute a major cause of derailments in the United States. Derailments are often very fatal and disastrous. While rail defect-caused derailments are becoming an increasing concern, the analysis of rail defects remain relatively unchanged. Weibull analysis has historically been the go-to distribution when it comes to analyzing rail defects irrespective of the type, tonnage, size or age. In other words, the analysis is not only defect-based, the defect distribution varies from section to section. No free-lunch theorem in statistical learning provides the basis to assert that a single statistical technique is not guaranteed to perform excellently on all defect types because all techniques will perform almost equally if their performances are aggregated on all possible data types. Hence, the use of Weibull distribution is not only ideal for all defect types. It also does not provide a network infrastructure manager the information needed to allocate resources for each defect type. More so, Weibull distribution like most other distributions belong to the beta family which is trained, fitted and tested on the same data-set. In this study, we present an agglomerated machine learning of defects as an alternative to Weibull Analysis of rail infrastructure. This methodology considers different rail defect prediction techniques using Stacking Ensembles in a way that training and testing is done on different data splits. We considered a database of over 20 miles of rail defect and track geometry data spanning approximately 5 years. Our results provide an infrastructure tool developed to assist rail owners and infrastructure managers a decision-making rationale for resource allocation and maintenance rather than the micro-level decision making influenced by the Weibull for limited sections of track.