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Knowledge Automation Through Graph Mining, Convolution, and Explanation Framework: A Soft Sensor Practice
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DOI：10.1109/TII.2021.3127204.png)
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In industrial processes, data-driven soft sensors have played an important role for the effective process control, optimization, and monitoring. Deep learning technique has been widely used in soft sensor field in recent years for its excellent feature representation capability in spatial and temporal scales. However, the shortcomings for deep learning technique seriously hinder its application in industrial processes. For example, the knowledge cannot be added into the model, and the model prediction could not be well explained. To solve those problems, the graph mining, convolution, and explanation framework is proposed for knowledge automation in this article. Based on the equivalence analysis of the self-attention mechanism (SAM) and graph convolution (GC) operation, the spatial SAM is adopted for knowledge discovery from data directly. After that, the GC layer considering the relationship between process variables can utilize the knowledge for constructing soft sensor models. Besides, to explain which knowledge contributes to the final model prediction, the graph neural network explainer is designed for explaining the model output. Finally, the effectiveness and feasibility of the framework are evaluated on an industrial process, in which the knowledge discovered from the data is of great consistence with the prior knowledge, and the final explanation indicated that most of the knowledge is consistent with the prior knowledge contributed to the prediction.
Keyword:
Convolution
Predictive models
Knowledge discovery
Feature extraction
Mathematical models
Informatics
Deep learning
Deep learning (DL)
graph convolution (GC)
graph mining
model explanation
soft sensor
期刊
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9.9
论文数:
8.6K
被引数:
6.0W
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