Complex probabilistic slow feature extraction with applications in process data analytics

• DOI: 10.1016/j.compchemeng.2021.107456
• Publication Date: 2021-07-27T07:48:06Z
• Authors: Vamsi Krishna Puli; Rahul Raveendran; Biao Huang
• Keywords: 02 engineering and technology; 0204 chemical engineering

Abstract Today, in modern industrial processes, thousands of correlated process variables are measured and stored. Dimension reduction techniques are often employed to construct informative features by discarding redundant information. Slow feature analysis is one such technique that extracts the slowly varying patterns from measured data. Oscillatory behaviour is prevalent in process data due to inadequate control loop tuning and external disturbances such as diurnal temperature variation. Extracting these oscillatory patterns is vital in applications such as control loop monitoring, fault diagnosis. Slow feature analysis may not extract oscillating patterns when the signal to noise ratio is low in process data. This paper proposes the complex probabilistic formulation that extracts slow oscillatory features. We also present the Expectation-Maximization algorithm to obtain the optimal parameter estimates. Finally, three case studies are presented to illustrate the efficacy of the proposed formulation in soft sensing and fault detection applications.