4.7 Article

Dynamic Security Assessment Framework for Steel Casting Workshops in Smart Factory

Journal

IEEE TRANSACTIONS ON RELIABILITY
Volume -, Issue -, Pages -

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TR.2022.3224732

Keywords

Sensors; Steel; Visualization; Casting; Monitoring; Feature extraction; Task analysis; Dynamic security assessment (DSA); interaction representations; recurrent neural network (RNN); scene recognition; steel casting

Funding

  1. National Natural Science Foundation of China [62072155]
  2. Six talent peaks project in Jiangsu Province [XYDXX-012]
  3. Natural Science Foundation of Jiangsu Province [BK20221230]
  4. Guangxi Key Laboratory of Cryptography and Information Security [GCIS202110]

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In this article, a novel framework for security assessment (SA) in smart factories is proposed. The framework uses visual sensing to automatically detect objects and employs graph convolutional network and recurrent neural network to capture key interactions and evaluate security.
Security assessment (SA) system is crucial to ensure the production safety of a smart factory with rapid development of artificial intelligence. In this article, we propose a novel SA framework. Different from the conventional static monitoring systems based on traditional sensing technologies, the proposed framework can automatically detect objects via visual sensing. We use a skeleton-based graph convolutional network to generate action vocabulary for the intermediate representations of action-to-action cooccurrence relations. These representations are encoded into the sequential interaction models to form the interaction representations. Integrating the states of molten steel levels as the reference labels, the sequential representations are fed into a recurrent neural network model with multilayer gated recurrent units (GRUs) to capture the key interactions leading to the accidents, in which an attention mechanism is used to reweight the actions and eliminate the invalid interactions. The predicted labels and the hidden states of the scenes are passing among multilayer GRUs. Finally, we optimize the global output to dynamically assess the security by calculating a joint objective function with a regularized cross-entropy loss. On the self-collected dataset from our partner Iron and Steel company and on-line video clips, the proposed framework performs better than the existing SA schemes.

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