document: Learning to predict train wheel failures This paper describes a successful but challenging application of data mining in the railway industry . The objective is to optimize maintenance and operation of trains through prognostics of wheel failures . In addition to reducing maintenance costs , the proposed technology will help improve railway safety and augment throughput . Building on established techniques from data mining and machine learning , we present a methodology to learn models to predict train wheel failures from readily available operational and maintenance data . This methodology addresses various data mining tasks such as automatic labeling , feature extraction , model building , model fusion , and evaluation . After a detailed description of the methodology , we report results from large-scale experiments . These results clearly show the great potential of this innovative application of data mining in the railway industry .
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