Railcar Bogie Performance Monitoring using Mutual Information and Support Vector Machines

Abstract


 
 
 Railcar condition monitoring is an area of high importance and global relevance. The economic and safety concerns of equipment maintenance in North America mandate efforts in prognostics and health management. This paper presents the results from the development of a vibration based condition monitoring algorithm for freight rail, utilizing mutual information feature selection and support vector machine classification of bogie component faults. The algorithm is an implementation of a previously proposed railcar condition monitoring solution by the authors. The proposed monitoring solution is a data-driven method which was developed with measurements taken at a railroad test laboratory under controlled conditions. Vibration data was collected from multiple locations on a railcar over several test runs, each utilizing wheelsets with different levels of wear. The input of controlled wheel wear levels was aimed at varying the system outputs to resemble those of cars with different levels of mileage in revenue service. The generated data sets were processed and a feature set was extracted from the acceleration signals. The data was divided into training and validation partitions using a cross validation scheme to preserve the sequence for both sets. A mutual information (MI) estimation algorithm was used to rank the features based on their similarity to the classified fault state. Both the optimized feature set from the MI feature selection algorithm as well as the full, non-discriminate feature set were used as inputs to the support vector machine to assess classification accuracy. The results of this assessment are presented in the paper along with a presentation of the methods. The paper concludes with a proposal for a monitoring strategy aimed at specifically detecting faulty components and practicing predictive maintenance.