Data-driven fault detection and diagnosis in industrial process systems: A systematic review and perspective

Physics-informed ensemble learning for hierarchical fault diagnosis in quadruped robots

Deep learning framework for fault detection and diagnosis in grid-connected PV systems using GAN-based data augmentation

Multi-channel convolutional neural network and decision-level fusion for multi-part cover fault diagnosis

A fault diagnosis method based on diffusion model and 2D-CNN for small sample conditions: Application to reciprocating pumps

The worldwide effort to reach carbon peak and neutrality objectives alongside energy market expansion has sped up renewable energy integration, like wind and solar power. The shift towards renewable energy integration introduces substantial uncertainties in power system scheduling and control processes, which test the limits of existing theoretical methods. The advanced reasoning and data-processing capabilities of Large Language Models (LLMs), with particular reference to their ability to analyze multimodal data, provide transformative potential for managing and controlling smart grids. This review examines how LLMs can tackle modern power system challenges while confirming their fit with the power sector’s expanding dependency on Artificial Intelligence (AI) technologies. We assess the requirements of modern power systems for such AI-based solutions, while evaluating how LLMs shape grid management and exploring their enabling technologies, such as model architecture and training methods, along with necessary data. Our review investigates how multimodal LLM technology serves different smart grids’ functions, including generation, transmission, distribution, consumption, and equipment management, to exhibit its adaptable nature in strengthening grid resilience and efficiency. • This review explores the role of multimodal Large Language Models (LLMs) in smart grid management, showing how their ability to integrate and process different types of data, including sensor readings, text logs, weather forecasts, and equipment images, can significantly improve decision-making, fault diagnosis, and operational planning in power systems. • The study analyzes the architectural and training aspects of multimodal LLMs, including the use of pretrained modular encoders, efficient fine-tuning methods such as Low-Rank adaptation (LoRA), and specialized loss functions, highlighting how these techniques enable adaptation to the specific needs of smart grid applications without lengthy retraining. • Practical considerations for industrial implementation are examined, covering multimodal data collection and preprocessing, domain-specific knowledge integration, intelligent task decomposition, and system-level integration, illustrating how LLMs can be seamlessly integrated into power system operating environments. • The review highlights the potential of multimodal LLMs to improve the resilience of the power grid, optimize the integration of renewable energy, and support human-machine collaboration, while outlining future research directions, such as domain-specific base models, physics-based architectures, and human-in-the-loop feedback, in order to further improve reliability and interpretability in critical infrastructure applications.

Time spectrum repaint domain-wise diffusion model for bearing fault diagnosis under time-varying conditions in high-speed trains.

Experimental study on the application of stack voltage distribution fluctuations in operational monitoring and fault diagnosis: Analyzing the effects of stack degradation on monitoring accuracy and validating it in an 80 kW fuel cell stack

Masked autoencoders-based fault diagnosis pretraining model with meta fine-tuning and prototype prompting for few-shot fault diagnosis

Temporal causal discovery-enhanced hierarchical monitoring for root cause diagnosis of aero-engine faults

A generalized zero-shot bearing fault diagnosis method under unseen faults and variable operating conditions

Integrated design for active fault diagnosis and fault-tolerant optimal control for stochastic systems with non-convex input constraints

A cross-domain rotating machinery fault diagnosis based on multi-source information progressive domain adaptation network

Dimensionless features and comprehensive fuzzy-based models for fault diagnosis of rolling element bearings under varying operating conditions.

A physics based data generation method using a dynamic model for wind turbine gearbox fault diagnosis in fault data scarce settings

A sensor and actuator fault diagnosis method for supercritical carbon dioxide direct cooled nuclear reactor system

Instance-level mutual contrast network for semi-supervised domain generalization fault diagnosis

A physics-informed generalization framework for cross-condition bearing fault diagnosis with limited labeled data

Prior-constrained dual-grained alignment assisted mutual information guided disentanglement network for cross-machine aero-engine bearing fault diagnosis

Early fault diagnosis and performance grading of electric ball valves in gas pipelines: Experimental study and field validation