Can Large Language Models Diagnose Machine Faults? A Comprehensive Survey from Deep Learning to Foundation Models

[TechRxiv] Wei Chen, Jiaming Zhang, Haowen Liu and others have published a review that traces the evolution of industrial intelligent fault diagnosis from traditional data-driven methods and deep learning to emerging large language models and foundation models. The paper states that over the past decade, convolutional neural networks, recurrent architectures and generative adversarial networks have significantly improved diagnostic accuracy for rotating machinery, power systems and manufacturing processes; large language models, meanwhile, offer a complement to or surpass these methods in natural language reasoning, multimodal fusion and few-shot generalization. The authors establish a taxonomy organized by signal-level feature extraction, supervised and self-supervised deep architectures, transfer and meta-learning strategies, and LLM pipelines combining prompting, retrieval-augmented generation and fine-tuning, and compare representative methods on standard benchmarks. The paper identifies open challenges including domain-specific pretraining, real-time deployment, multimodal alignment and interpretability, and sets out research directions.