Compression and trending are techniques widely used in the initial treatment of raw process data. Two popular methods, Box Car with Backward Slope and Swinging Door, were reviewed recently by Kennedy. In spite of their popularity, neither of these methods are designed to cope with process variability and outliers. They also require one or more parameters to be specified based on practical considerations. In this paper we propose a new method, Piecewise Linear Online Trending (PLOT), which is statistically based and which performs significantly better. Unlike the two existing methods, it adapts to process variability and noisy data, recognizes and eliminates outliers, and it is robust even in the presence of outliers. It fits the data better for the same number of trends. The fidelity of its performance may be fine-tuned with a single level of significance which may be set by the user without requiring any expertise in statistics. It may be used in an online or a batch mode, and interfaces easily with most existing packages.
ASJC Scopus subject areas
- Chemical Engineering(all)
- Computer Science Applications