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Feature Representation-Based Cross-Modality Shared-Specific Network and Its Application in Multimode Process Soft Sensing
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AI
DOI：10.1109/TII.2024.3414488.png)
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摘要
En 中文
As the production demand and external environment change, the same production process may have multiple stable working conditions, i.e., multimode process. The traditional process monitoring methods cannot be directly applied to industrial data with multipeak distribution. In order to address the multimode process monitoring problem, a cross-modality shared-specific network (CMSS-Net) is proposed in this article. First, to address the problem of unavailability of mode indicator variable, CMSS-Net adds a loss term based on discriminative idea to the loss function, which improves the mode recognition ability by maximizing the interclass distance and minimizing the intraclass distance. The multimode process is then monitored. Since different modes originate from the same production process, there exists some common information among modes. CMSS-Net extracts the shared information by minimizing the difference in the distribution of features among modes. At the same time, the gating mechanism is used to fuse the shared information between modes and the unique features of the modes into a multivariate feature fusion. This improves the performance of the model due to the information being enriched across the modes, while increasing the transparency of the model's decision-making process. Finally, the proposed method is developed as soft sensors for Tennessee Eastman process and power plant gas turbine emission process. It is compared with some popular methods. The experimental results demonstrate the effectiveness and superiority of CMSS-Net when applied to the multimode process.
Keyword:
Feature extraction
Data models
Monitoring
Data mining
Process control
Transfer learning
Production
Mode recognition
multimode process
shared features
soft sensors
transfer learning
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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引用论文
Mode Information Separated β-VAE Regression for Multimode Industrial Process Soft Sensing.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
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