Fault prediction models for big data analysis in industrial internet of things: A literature review - Zhou, Yang; Bashah, Nor Shahniza Binti Kamal (Edelweiss Applied Science and Technology, 2025)
Over time, fault prediction methods have seen significant advancements, becoming integral to ensuring the seamless operation of complex modern systems. These innovations are vital for controlling costs and improving safety in industries that rely on high-value assets, including sectors like oil and gas, manufacturing, and power generation. This paper primarily aims to investigate the evolution of various fault prediction techniques, highlighting their real-world applications and benefits across different industries. It provides an in-depth and up-to-date review of state-of-the-art fault detection models, focusing particularly on their deployment in industrial environments. To achieve this, advanced bibliometric approaches are used to analyze over 500 peer-reviewed articles published after 2010. A preliminary exploratory analysis identifies key players, influential authors, leading countries, and other key metrics. Additionally, a co-citation network analysis is performed to uncover and visualize key research clusters in the field. A thorough content analysis of the 100 most-cited papers follows, aiming to chart the evolution of fault detection strategies and the growing influence of artificial intelligence-based algorithms in various industrial domains. The study’s findings offer valuable insights into the historical progression of fault detection methods, emphasizing their reliability while highlighting the increasing importance of intelligent algorithms. This research presents a unique perspective on the potential future directions of fault detection, providing a roadmap for researchers, policymakers, and industries with significant asset dependencies.