Complex probabilistic slow feature extraction with applications in process data analytics

Puli, Vamsi Krishna; Raveendran, Rahul; Huang, Biao

doi:10.1016/j.compchemeng.2021.107456

Cited by 20 publications

(6 citation statements)

References 43 publications

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“…Penicillin fermentation is a biochemical process, and the products of this process play a crucial role in the biomedical field. [ 12 ] The data used in this section were derived from a penicillin fermentation process designed by Birol et al [ 35 ] in which two main phases were simulated, including the cell culture and replenishment fermentation phases. In order to improve the efficiency of penicillin production, effective process monitoring is required, of which penicillin concentration is usually one of the most critical indicators.…”

Section: Case Studymentioning

confidence: 99%

“…In LVMs, the original data is projected into the latent space, which preserves key features and reduces noise disturbance. Traditional LVMs include principal component analysis (PCA), [9,10] probabilistic slow feature analysis (PSFA), [11][12][13] partial least squares (PLS), [14,15] and independent component analysis (ICA). [16,17] Over the past decades, soft sensing methods based on the mentioned models have been successfully developed for industrial processes.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes

Zhao,

Xu,

Wang

2023

Can J Chem Eng

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Benefitting from the development of industrial intelligence, data‐driven soft sensors have been widely applied in industrial processes in recent years. Driven by fluctuations in raw material quality, varying loads, and uncertain disturbances, industrial processes are often characterized by nonstationary properties. However, the nonstationary characteristics of the process are not considered in traditional data‐driven methods, leading to the poor prediction performance of soft sensors. Meanwhile, numerous nonstationary modelling methods have been proposed for process monitoring, with probabilistic stationary subspace analysis (PSSA) showing significant application potential. Unfortunately, PSSA has not yet been used to develop effective soft sensors for nonstationary processes. Therefore, the PSSA model is extended to the regression form (PSSR), and a corresponding soft sensing model is developed in this paper. Unlike previous approaches, the proposed PSSR can offer a suitable description of nonstationary processes and fully capture the mathematical correlation between input and output variables. Finally, the prediction performance of PSSR is verified through case studies on a numerical example and penicillin fermentation process. The experimental results indicate that PSSR can provide a superior solution for soft sensing modelling of nonstationary industrial processes.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes

Zhao,

Xu,

Wang

2023

Can J Chem Eng

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“…• As the state-transition matrix A is diagonal, PSFA cannot accommodate complex poles and thus, cannot extract the oscillating patterns. CPSFA [20] relaxed the diagonal matrix to block-diagonal structure to accommodate complex eigenvalues and encode oscillating features naturally, as shown in (11).…”

Section: B Probabilistic Extensionsmentioning

confidence: 99%

“…Since the slow feature transition matrix A is assumed to follow the structure given in (11), the resultant covariance matrix Q is block diagonal, as shown below. The condition shown in (20) must be satisfied for the positive definiteness of the covariance matrix Q.…”

Section: A Data Generating Modelmentioning

confidence: 99%

“…Several deterministic [18] and probabilistic [19] extensions were proposed to extract quality-relevant slow features. Recently, complex probabilistic slow feature analysis [20] was proposed to model the stationary oscillatory patterns explicitly in the feature space. Further, a variational Bayesian approach [21] has been presented to separate the drift-type non-stationary behaviour and slow oscillating features from non-stationary data.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Semi-supervised Inference Networks for the Extraction of Slow Oscillating Features

Puli¹

2022

Preprint

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<p>Due to physical and monetary constraints, quality-related variables in the process industries are generally difficult to measure using hardware sensors. Therefore, they are often not available as frequently as the other variables since obtaining them typically via laboratory analysis are time-consuming. On the other hand, the dynamic nature of easy-to-measure process data contains valuable predictive information about the quality variables. Consequently, modelling sequential data is beneficial in building a soft sensor that can predict the quality variable. Several techniques were proposed to extract dynamic features as measured data often suffers from noise and collinearity. This paper presents a semi-supervised oscillating slow feature inference network to address certain shortcomings of the existing solutions. The proposed learning algorithm uses a gated recurrent unit to learn a combined inference network for missing quality variable imputation and oscillating slow feature extraction from nonlinear process data. We evaluate the efficacy of the proposed methodology on a simulated and an industrial process.</p>

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Fault diagnosis of air handling unit via combining probabilistic slow feature analysis and attention residual network

Li,

Yu,

Shang

et al. 2023

Neural Comput & Applic

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Complex probabilistic slow feature extraction with applications in process data analytics

Cited by 20 publications

References 43 publications

Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes

Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes

Nonlinear Semi-supervised Inference Networks for the Extraction of Slow Oscillating Features

Fault diagnosis of air handling unit via combining probabilistic slow feature analysis and attention residual network

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