Robust On-Line Digital Differentiation with an Application to Underground Coal Mining

Abstract In this paper we develop an algorithm for robust on-line rate estimation of noisy digital signals. The algorithm is based on Kalman filtering techniques and consequently has the capability of coping with noise and signal discontinuities. This algorithm is then successfully applied to hydraulic leg pressure data obtained from mobile mechanised roof supports (breaker line supports) in an underground coal mine. The data is collected by a monitoring system (BLSmon) which allows real time data processing to aid in determining the onset of roof caving events.