A novel exergy-related fault detection and diagnosis framework with transformer-based conditional generative adversarial networks for hot strip mill process

Abstract

Fault detection and diagnosis (FDD) plays a crucial role in the iron and steel industry. However, the iron and steel industry have unique complex characteristics such as high energy consumption, temporal and spatial correlation, and data imbalance, which makes the application of traditional FDD methods ineffective. With the development of FDD, deep learning-based methods have attracted more and more attention. To address the above issues, this work combines the advantages of transformer and conditional generative adversarial networks (CGAN) to propose a novel exergy-related FDD framework with transformer-based CGAN (TransCGAN). Firstly, we extract exergy-related variables and convert one-dimensional data into image data to preserve spatiotemporal information. Next, the transformer encoder is used as the basic block of CGAN to capture the relationship between normal and fault data, and generate fault samples. Then, the generated data and original data are mixed to train the proposed FDD model. Finally, the feasibility of TransCGAN is verified by the hot strip mill process (HSMP). Compared with other state-of-the-art (SOTA) methods, the proposed method has higher fault detection rate and better fault diagnostic performance.