A Systematic Review of Intelligent Agents, Language Models, and Recurrent Neural Networks in Industrial Maintenance: Driving Value Creation for the Mining Sector

Abstract

This PRISMA 2020–compliant systematic review examines how intelligent agents, large language models (LLMs), and recurrent neural networks (RNNs) can be combined for industrial maintenance, with a sector‐specific focus on mining. Scopus and Web of Science (2018–2025) were searched using replicable queries, and a dual text‐representation pipeline (TF–IDF with bi/trigrams and sentence‐transformer embeddings) was applied. Model selection scanned k over a predefined grid with internal indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz), and robustness was assessed through multiseed stability, bootstrap consensus, representation‐sensitivity checks, and a control run with HDBSCAN. Study quality and risk of bias were appraised with an AI‐and‐control–oriented matrix (ACE‐QA). Two macroclusters emerged. The first centers on distributed control, consensus and formation, fault tolerance, observers, and learning‐based designs (fuzzy/neural/RL), including finite/predefined‐time and event/dynamic event–triggered mechanisms. The second addresses secure and resilient cooperation under cyber threats (DoS, deception, and FDIA), integrating observer‐based estimation and communication‐efficient protocols. Cross‐cutting findings indicate that event‐triggered updates reduce bandwidth and compute requirements, while robust estimation and fault‐tolerant control improve availability under harsh conditions and intermittent networks—typical in mining. A maturity map suggests high technical readiness and growing adoption for RNN‐based sensing analytics, advancing readiness but emerging adoption for multiagent coordination, and early adoption of LLMs for text‐grounded maintenance intelligence. Evidence gaps persist in replicability, cross‐site transfer, uncertainty reporting, and mining‐grade validation at the edge. A design agenda is outlined that prioritizes digital‐twin stress testing, edge‐first evaluation of agent coordination, secure‐by‐design pipelines (authenticated/encrypted messaging and adversarial testing), and shift‐aware validation. In sum, a hybrid stack—RNNs for perception, LLMs for knowledge grounding, and agents for coordinated action—offers a practical route to reliable, secure, and communication‐efficient predictive maintenance in Mining 4.0.

Topics & Concepts