Shape anomaly detection for process monitoring of a sequencing batch reactor

Anomaly detection is critical to process modeling, monitoring, and control since successful execution of these engineering tasks depends on access to validated data. Classical methods for data validation are quantitative in nature and require either accurate process knowledge, large representative data sets, or both. In contrast, a small section of the fault diagnosis literature has focused on qualitative data and model representations. The major benefit of such methods is that imprecise but reliable results can be obtained under previously unseen process conditions. This work continues with a line of work focused on qualitative trend analysis which is the qualitative approach to data series analysis. An existing method based on shape-constrained spline function fitting is expanded to deal explicitly with discontinuities and is applied here for the first time for anomaly detection. An experimental test case and a comparison with the principal component analysis method bear out the benefits of the qualitative approach to process monitoring.