Model Based Signal Processing for Capacitive Ice Sensing: Algorithm and Demonstration for Field Data

摘要

Atmospheric ice accretion has been reported a severe safety issue for technical systems such as communication infrastructure, power lines, blades of wind turbines, etc. in cold climate regions. Among different sensing systems to detect and provide reliable information about the state of ice, capacitive sensing has been reported as a suitable technique. In capacitive sensing, measured capacitances are influenced due to ice accretion. Signal processing for ice sensors requires to estimate parameters for the state of ice from the data. In this paper we present a model based estimation approach for capacitive ice sensors. We will show how the measurement process can be modeled and demonstrate the construction of estimation algorithms within the Bayesian framework. We will demonstrate the capability of our approach for automated ice detection by means of long term field data showing the ability to estimate ice layers with precision better than 1 mm.