Early detection of railcar bearing failure-a system architecture perspective

摘要

A powerful, innovative diagnostic technique for assessing railcar wheel health characteristics is to listen to the operating sound of a bearing. The emission of acourstic energy (sound) from a wheel bearing is a frequency-dependent and load-related phenomenon. A complementary set of bearing health data to audible acoustic emission (AE) data, referred to as "stress-wave" data, exists at an order of magnitude above traditional vibration-based (i.e., accelerometer) data and contains information about friction and shock conditions in a bearing under highly loaded conditions. These health-realted symptoms of shock and friction will provide an early warning that can be used to prevent flat wheel failures and train derailments through condition-based maintenance (CBM). The system architecture aspects of incorporating a bearing health monitoring system on existing and future freight cars are described.