Sensor fusion: the application of soft computing in monitoring and control for railroad maintenance

We attempt to identify which sensor fusion techniques are among the most promising ones. The starting point of our discussion is the observation that the added value of fusion of similar sensors must originate from a nonlinear combination of sensor data streams. This observation naturally gives rise to the application of nonlinear models, e.g., from the area of soft computing, viz. fuzzy logic, neural networks and evolutionary programming. At Strukton a sensor fusion approach for railroad maintenance management has been adopted, in which an integrated approach towards sensor optimization, sensor management, and early fusion is pursued. In this way one may hope to attain the goal of sensor fusion, viz. an improved situation assessment and early warning to trigger preventative maintenance.