Small Moving Window Calibration Models for Soft Sensing Processes with Limited History
This work addresses the challenge of accurate soft sensing in industrial processes with limited data, offering incremental improvements for domain-specific applications.
The study compared five soft sensor methods for processes with limited historical data, finding that small window sizes minimized prediction errors on two datasets and that a novel RF-PLS ensemble achieved the lowest one-step-ahead errors and greater stability at larger delays compared to other methods.
Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of $y$, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online. It was found that, on two of the datasets studied, small window sizes led to the lowest prediction errors for all of the moving window methods studied. On the majority of datasets studied, the RF-PLS calibration method offered the lowest one-step-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability at larger time delays than moving window PLS alone. It was found that both the random forest and RF-PLS methods most adequately modeled the datasets that did not feature purely monotonic increases in property values, but that both methods performed more poorly than moving window PLS models on one dataset with purely monotonic property values. Other data dependent findings are presented and discussed.