Using hidden Markov model to identify oscillation temporal pattern for control loops

Oscillations in the control loops can result in high variability of product quality, more energy consumption, and accelerated equipment aging. The existing oscillation detection methods use only part information of signal, for example, the regularity of zero-crossings, but do not take the temporal sequence into account. This research studies the introduction of a priori structure to extract the repeated pattern from a time series based on the hidden Markov model (HMM), particularly in recognition of the oscillation behavior. To get the representative hidden state sequence, a strategy of merging hidden states to find a good HMM structure for a given time sequence is developed. Then a new framework for analyzing the hidden state series data called oscillation detection algorithm (ODA) is presented. This ODA adapts innovative data mining concepts to analyze the state series data. In particular, it can reveal hidden temporal patterns. Case studies on a numeric example and a refinery process are performed to illustrate the effectiveness of the proposed method.