A control-oriented operation mode recognizing method using fuzzy evaluation and attention LSTM networks

Operation mode recognition is a prerequisite for precise regulation of key performance indicators (KPIs) in industrial processes. However, system uncertainties and the complexity of the process dynamics pose significant challenges in achieving accurate mode partitioning. This study proposes a contro...

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Bibliographic Details
Published in:Applied soft computing Vol. 180; p. 113326
Main Authors: Sun, Bei, Peng, Zhixuan, Dai, Juntao, Li, Yonggang
Format: Journal Article
Language:English
Published: Elsevier B.V 01.08.2025
Subjects:
Attention mechanism
Indicator regulation potential
LSTM autoencoder
Monte Carlo simulation
Operation mode recognition
Attention mechanism
Indicator regulation potential
LSTM autoencoder
Monte Carlo simulation
Operation mode recognition
ISSN:1568-4946
Online Access:Get full text
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Summary:Operation mode recognition is a prerequisite for precise regulation of key performance indicators (KPIs) in industrial processes. However, system uncertainties and the complexity of the process dynamics pose significant challenges in achieving accurate mode partitioning. This study proposes a control-oriented operation mode recognition method called attention-long short-term memory-Monte Carlo simulation (AT-LSTM-MC). First, a fuzzy inference-based ‘indicator regulation potential’ evaluation framework is established to quantitatively describe the maximum control potential of each control variable on KPIs. Subsequently, considering the temporal dependencies of industrial process data, a long short-term memory (LSTM) autoencoder network is employed as the core architecture for feature extraction, where the ‘indicator regulation potential’ guides the LSTM autoencoder through attention layers to extract control-oriented deep clustering features. Finally, the K-means clustering method is utilized to determine the system operation modes based on these deep clustering features. To address uncertainty-induced challenges, multiple Monte Carlo simulations are performed on the operation mode recognition for the same period, thereby obtaining a statistically convergent operation mode. The effectiveness of the proposed method is validated through a case study of an actual industrial process. •A ‘indicator regulation potential’ assessment framework is proposed.•‘Indicator regulation potential’ quantifies control variables’ max impact on KPIs.•Developing a control-oriented industrial process operation mode recognition method.•Validating the proposed method in an industrial process with inlet uncertainties.
ISSN:1568-4946
DOI:10.1016/j.asoc.2025.113326