The 11th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (KDD 2005), August 21-22, 2005, Chicago, Illinois, USA
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.
Proceedings of the 11th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (KDD 2005) [Proceedings].