Detecting and isolating plant-wide oscillations via slow feature analysis

Abstract

This paper aims at detecting and isolating multiple sources of oscillations in control loops via slow feature analysis. The control loops in the process industries are usually coupled, and therefore disturbances can propagate to downstream process variables through energy or material flows and thus plant-wide disturbances arise. A significant portion of disturbances are oscillatory, and the root causes may be poor controller design or equipment faults such as valve stiction. It is important to find out locations of these oscillation sources so that further root cause diagnosis is possible. A new technique termed as slow feature analysis (SFA) is applied to detect plant-wide oscillations and isolate the sources at the loop level. SFA can recover slowly varying source signals from observed data. Since most oscillations in the process industries have low oscillatory frequencies, SFA is a very powerful tool to recover oscillation sources from observed process data. Two projection-based indices, CCI and CSI, are derived to investigate how the control loops are affected by the oscillations and isolate oscillation sources at the loop level. A simulation case study is presented to demonstrate the effectiveness of the proposed method.