Orthogonal long short-term memory autoencoder for semi-supervised soft sensor modeling

Data-driven soft sensor methods are popularly applied to predict hard-to-measure variables in industrial production processes. However, in practice, the number of labeled samples is limited, which will affect the accuracy of developed soft sensors. Aiming at this point, semi-supervised soft sensor m...

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Vydané v:Chemometrics and intelligent laboratory systems Ročník 265; s. 105499
Hlavní autori: Ma, Fangyuan, Ji, Cheng, Wang, Jingde, Sun, Wei, Romagnoli, Jose A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 15.10.2025
Predmet:
Data-driven method
Dynamic feature extraction
Feature selection
Machine learning
Orthogonal auto encoder
Feature selection
Dynamic feature extraction
Machine learning
Orthogonal auto encoder
Data-driven method
ISSN:0169-7439
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Popis
Shrnutí:Data-driven soft sensor methods are popularly applied to predict hard-to-measure variables in industrial production processes. However, in practice, the number of labeled samples is limited, which will affect the accuracy of developed soft sensors. Aiming at this point, semi-supervised soft sensor methods are proposed that combine unsupervised feature extraction and supervised mapping correlation establishment. Auto encoder (AE) is a commonly used feature extraction method for effectively capturing the nonlinear features of processes from unlabeled data. Since typical AEs have no special constraints on the output of latent space, there could be redundancy among the extracted features, which will increase the complexity of mapping correlation establishment. Meanwhile, the dynamic features of processes are difficult to extract by typical AE. Both issues could affect the performance of soft sensors. To address these issues, an Orthogonal Long Short-Term Memory Auto encoder (OLAE) is proposed in this work. By adding the orthogonal constraint on latent space output to the loss function of Long Short-Term Memory Auto encoder, orthogonal dynamic features can be obtained. Then, the OLAE is employed in the feature extraction stage. Using Chatterjee's New Coefficient, orthogonal features related to hard-to-measure variables are screened out for mapping correlation establishment. Considering the limited number of labeled data samples, a prediction model based on support vector regression is established to realize the prediction of hard-to-measure variables. Data from a penicillin fermentation process and an industrial cracking furnace are investigated to evaluate the effectiveness of the proposed soft sensor method. •A novel semi-supervised soft sensor method called SS-OLAE is developed.•An orthogonal LSTM AE (OLAE) is proposed for extracting orthogonal dynamic features.•The use of OLAE solves the issue of information redundancy in extracted features.•Dynamic information in both labeled and unlabeled data can be extracted and used.•SS-OLAE is validated on a simulation process (Pensim) and an industrial process.
ISSN:0169-7439
DOI:10.1016/j.chemolab.2025.105499