Generative adversarial network-based semi-supervised learning for real-time risk warning of process industries. (15th July 2020)

• Record Type: Journal Article
• Authors: He, Rui; Li, Xinhong; Chen, Guoming; Chen, Guoxing; Liu, Yiwei
• Is Part Of: Expert systems with applications. Volume 150(2020)
• Publication Date: 2020-07-15
• Subjects: Risk warning -- Deep learning -- Generative adversarial networks -- Semi-supervised learning -- Fuzzy HAZOP -- Multizone circulating reactor; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113244
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Abstract: Due to the non-cognition of real-time data, rare loss-based risk warning methods can effectively respond to unexpected emergencies. Machine learning has powerful data processing capabilities and real-time computing functions and thus is suitable for offsetting the shortcomings of traditional risk methods. Risk analysis can be easily employed to perform risk-based data classification for a set of process data. However, the risk analysis process is too complicated to label risk levels for all processes, which is hard to satisfy the requirements of the amount of data for supervised learning. Therefore, the present paper focuses on developing semi-supervised learning methods for the construction of real-time risk-based early warning systems. By using fuzzy HAZOP, we estimate the risk of systems quantitatively based on the process data. With the consideration of scarce labeled data and numerous unlabeled information, we develop the generative adversarial network (GAN)-based semi-supervised learning method to identify the process risk timely. Besides, deep network architecture integrated with the convolutional neural network (CNN) is used for the codification of multi-dimensional process data to enhance the generalization of warning models. Finally, the effectiveness of the proposed method is evaluated through a comparative study with different algorithms on a case of multizone circulating reactor (MZCR).