Alarm prediction in large-scale sensor networks - A case study in railroad

Abstract

Sensor network is broadly used across industries to monitor equipment conditions. Huge volume of information collected from a large set of sensors poses great challenges to make inferences for prediction of significant events. We collaborate with a US Class I railway company and apply advanced analytics techniques to be able to predict alarms associated with catastrophic equipment failures several days ahead of time. We use the case study in railroad to demonstrate the techniques to address the big data concerns. In addition, the alarm-prediction rule development needs to satisfy the critical constraints to meet the high standards of prediction accuracy combined with human interpretability in railroad industry. We build customized SVM algorithm to meet the requirements. By adding a unique and remarkable feature of human interpretability to the rules we develop, our solution is able to facilitate the decision making process of operators and lead to efficient operational decision support. © 2013 IEEE.