Complex probabilistic slow feature extraction with applications in process data analytics

Vamsi Krishna Puli

doi:10.1016/j.compchemeng.2021.107456

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Complex probabilistic slow feature extraction with applications in process data analytics

2021-11-01

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Vamsi Krishna Puli

Rahul Raveendran

Biao Huang *

DOI：10.1016/j.compchemeng.2021.107456

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Abstract

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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 re-dundant information. Slow feature analysis is one such technique that extracts the slowly varying pat -terns 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 pro-poses 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 stud-ies are presented to illustrate the efficacy of the proposed formulation in soft sensing and fault detection applications. (c) 2021 Elsevier Ltd. All rights reserved.

Keywords:

Frequency analysis

Latent variable models

Oscillations detection and diagnosis

Slow feature analysis

Soft sensing

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