Advanced Deep Learning Techniques for Intelligent Fault Detection in Chemical Process Industries

Abstract

Human-friendly analysis of chemical process industries is a nonlinear dynamic system. This paper also points out a great research issue of smart and scalable monitoring methods as the traditional statistics-based and model-driven methods are often not sufficient in discovering complex relationships with high-dimensional data due to the gradual digitalization of industry. The purpose of this paper is to review and analyses existing work on deep learning-based methods for chemical processes monitoring and fault detection. This method provides an overview of approaches based on architecture like the autoencoder, CNN, RNN, and hybrid models and their deployment in benchmark scenarios or real industrial applications. The results indicate that deep learning models enhance fault diagnosis performance via automatic feature extraction, early anomaly detection and effective modelling of temporal dependencies. Hybrid and attention-based models fast-track robustness and diagnostic capability even further. Emphasizing the practical significance of deep learning that extends beyond traditional use cases, the study mentions areas such as predictive maintenance and process safety and operational optimization. It applies for United Nations Sustainable Development Goals (SDG 9, SDG 12, SDG 7), realize any kind of industry with high efficiency and sustainability.