Scale-space filtering for qualitative interpretation of real-time process data

This paper describes a qualitative interpretation method, which is used for extracting qualitative information from numeric sensor data. Firstly, whet her any change has occurred in chemical process data is determined by using the CUSUM (CUmulative SUMmation) test. From the sign of the first derivati ves of the profess variables, sensor patterns can be classified into the se ven primitives. Secondly, extraction of the trends of the data employing th e modified scale-space filtering is performed. The recursive form reduces t he calculation cost of the real-time scale-space filtering and solves the e ndpoint problem. The proposed method was tested for artificial patterns and the simulated data of a evaporator process, and produced good results.