KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912176708517888 |
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| author | Tew, Hwa Hui Li, Gaoxuan Ding, Fan Luo, Xuewen Loo, Junn Yong Ting, Chee-Ming Ding, Ze Yang Tan, Chee Pin |
| author_facet | Tew, Hwa Hui Li, Gaoxuan Ding, Fan Luo, Xuewen Loo, Junn Yong Ting, Chee-Ming Ding, Ze Yang Tan, Chee Pin |
| contents | Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledge discovery graph Attention Network for effective Soft sensing (KANS). Unlike the existing deep learning soft sensor models, KANS can discover the intrinsic correlations and irregular relationships between the multivariate industrial processes without a predefined topology. First, an unsupervised graph structure learning method is introduced, incorporating the cosine similarity between different sensor embedding to capture the correlations between sensors. Next, we present a graph attention-based representation learning that can compute the multivariate data parallelly to enhance the model in learning complex sensor nodes and edges. To fully explore KANS, knowledge discovery analysis has also been conducted to demonstrate the interpretability of the model. Experimental results demonstrate that KANS significantly outperforms all the baselines and state-of-the-art methods in soft sensing performance. Furthermore, the analysis shows that KANS can find sensors closely related to different process variables without domain knowledge, significantly improving soft sensing accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_02015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes Tew, Hwa Hui Li, Gaoxuan Ding, Fan Luo, Xuewen Loo, Junn Yong Ting, Chee-Ming Ding, Ze Yang Tan, Chee Pin Machine Learning Artificial Intelligence Systems and Control Signal Processing Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledge discovery graph Attention Network for effective Soft sensing (KANS). Unlike the existing deep learning soft sensor models, KANS can discover the intrinsic correlations and irregular relationships between the multivariate industrial processes without a predefined topology. First, an unsupervised graph structure learning method is introduced, incorporating the cosine similarity between different sensor embedding to capture the correlations between sensors. Next, we present a graph attention-based representation learning that can compute the multivariate data parallelly to enhance the model in learning complex sensor nodes and edges. To fully explore KANS, knowledge discovery analysis has also been conducted to demonstrate the interpretability of the model. Experimental results demonstrate that KANS significantly outperforms all the baselines and state-of-the-art methods in soft sensing performance. Furthermore, the analysis shows that KANS can find sensors closely related to different process variables without domain knowledge, significantly improving soft sensing accuracy. |
| title | KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes |
| topic | Machine Learning Artificial Intelligence Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2501.02015 |