About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
IEEE ICMA 2014
Conference paper
Predicting failure times of railcar wheels and trucks by using wayside detector signals
Abstract
Nowadays railway networks are instrumented with various wayside detectors. Given massive amount of data collected from electronic wayside detectors, railcar failure prediction has recently attracted great attention in order to reduce rolling stock inspection and maintenance costs and improve railway safety. In this work, we present a methodology to predict the failure times of railcar wheels and trucks, by fusing sensor signals from three types of wayside detectors, including Wheel Impact Load Detector (WILD), Machine Vision (MV) systems, and Optical Geometry Detectors (OGD). In data preprocessing, missing values are handled by missForest, a Random Forest based nonparametric missing value imputation algorithm, and a variety of new features are generated to capture the signal characteristics. Several state-of-the-art regression models are built and compared to predict the lifetime of railcar wheels and trucks in a US national railway network. © 2014 IEEE.