Generative adversarial network-based semi-supervised learning for real-time risk warning of process industries
作者：
Highlights：
• Deep learning methods are developed for building real-time risk warning systems.
• GAN-based semi-supervised learning requires scarce labeled data.
• Semi-supervised model incorporates numerous unlabeled samples into evaluations.
• CNN architectures handle multi-dimensional HAZOP data to enhance warning accuracy.
• Semi-supervised model has better performance for industrial data training.
摘要
•Deep learning methods are developed for building real-time risk warning systems.•GAN-based semi-supervised learning requires scarce labeled data.•Semi-supervised model incorporates numerous unlabeled samples into evaluations.•CNN architectures handle multi-dimensional HAZOP data to enhance warning accuracy.•Semi-supervised model has better performance for industrial data training.
论文关键词：Risk warning,Deep learning,Generative adversarial networks,Semi-supervised learning,Fuzzy HAZOP,Multizone circulating reactor
论文评审过程：Received 7 April 2019, Revised 4 January 2020, Accepted 24 January 2020, Available online 24 January 2020, Version of Record 10 February 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113244
