Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Survey of Hallucination in Natural Language Generation

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Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models

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Large Language Models Encode Clinical Knowledge

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A plant-wide industrial process control problem

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Training Language Models to Follow Instructions with Human Feedback

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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

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Llama 2: Open Foundation and Fine-Tuned Chat Models

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FD-LLM: Large language model for fault diagnosis of complex equipment

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Enhancing diagnostic capability with multi-agents conversational large language models

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IIoT-enabled digital twin for legacy and smart factory machines with LLM integration

Gautam, A., Aryal, M. R., Deshpande, S., Padalkar, S., Nikolaenko, M., Tang, M., & Anand, S. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.03.022 · Cited by 64

A Survey on Applications of Large Language Model-Driven Digital Twins for Intelligent Network Optimization

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A Review of Large Language Models for Energy Systems: Applications, Challenges, and Future Prospects

Mirshekali, H., Shadi, M. R., Ladani, F. G., & Shaker, H. R. (2025) · IEEE Access · doi:10.1109/access.2025.3610994 · Cited by 64

Large scale foundation models for intelligent manufacturing applications: a survey

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

Zhao, S., Yang, H., Kareck, T. L., Khan, F. I., & Wang, Q. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.112159 · Cited by 45

The role of AI in detecting and mitigating human errors in safety-critical industries: A review

Gursel, E., Madadi, M., Coble, J. B., Agarwal, V., Yadav, V., Boring, R. L., & Khojandi, A. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110682 · Cited by 44

A novel multiaxial fatigue life prediction method based on multi-scale physical neural network

Gao, J., Pan, R., Yuan, Y., Zhou, J., Ding, H., & Kong, W. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.112049 · Cited by 37

Deep digital twin-powered large vision-language model for multi-scenario industrial fault diagnosis

Xu, Q., Wen, G., Lei, Z., Gu, S., Su, Y., Zhang, Z., & Chen, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103997 · Cited by 31

Smart Fault Detection, Classification, and Localization in Distribution Networks: AI-Driven Approaches and Emerging Technologies

Wang, J., Mokhlis, H., Mansor, N. N., Illias, H. A., Ramasamy, A. K., Wu, X., & Wang, S. (2025) · IEEE Access · doi:10.1109/access.2025.3596791 · Cited by 32

Brain-Like Cognition-Driven Model Factory for IIoT Fault Diagnosis by Combining LLMs With Small Models

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Probing a novel machine tool fault reasoning and maintenance service recommendation approach through data-knowledge empowered LLMs integrated with AR-assisted maintenance guidance

Liu, C., Song, J., Tang, D., Wang, L., Zhu, H., & Cai, Q. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103460 · Cited by 21

Large model for fault diagnosis of industrial equipment based on a knowledge graph construction

Zhuang, J., Yang, J., Weigang, L., Chen, J., Zheng, Y., & Chen, Z. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113936 · Cited by 20

AI Large Models for Power System: A Survey and Outlook

Yao, Q. J., Fang, F., Chen, Y., Liu, J., Mo, H., & Ao, Y. (2025) · IET Smart Energy Systems · doi:10.1049/ses2.70000 · Cited by 20

LLMs in industrial domains: A systematic review of adaptation techniques and applications from the product lifecycle perspective

Yu, G., Wang, Y., Wang, Z., Chen, L., Zheng, Y., & Liu, Y. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104655 · Cited by 20

Wind power prediction using foundation large time series models enhanced by time series prompt in exogenous and tuning forms

Fan, Y., Song, T., Feng, C., Liu, C., & Jiang, D. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126535 · Cited by 19

Graph RAG-based fault diagnosis for train bogies using knowledge graphs and large language model

Miao, R., Wu, T., & Zhang, Z. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114855 · Cited by 21

Align knowledge with time-series: Cross-modal domain knowledge activation for LLM-enabled zero-shot fault diagnosis

Zhao, J., Zhao, C., & Yue, J. (2025) · Journal of Process Control · doi:10.1016/j.jprocont.2025.103534 · Cited by 17

A4PS: Agentic AI-assisted advanced planning and scheduling with large language models for smart manufacturing

Li, M., Zhou, Q., Li, W., Qu, T., Yang, M., & Jiang, P. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.01.003 · Cited by 23

An adaptive industrial large language model for mechanical fault diagnosis under variable operating conditions

Xu, C., Wang, Z., Jin, Y., & Nong, W. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104821 · Cited by 15

A MoE-LLM-based multisensor flexible fusion fault diagnosis method for rotating machinery

Lin, T., Ren, Z., Huang, K., Karimi, H. R., Zhu, Y., Feng, K., & Hong, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104009 · Cited by 13

Interpretable and robust fault diagnosis of rotating machinery in noisy environments via improved high-order spatial interactions network

Wang, B., Liang, P., Li, Y., Hu, J., & Zhang, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111568 · Cited by 12

Large language models and AI agents in disaster-resilient infrastructure: concepts, applications, pathways, and challenges

Cui, J., Zhai, C., Wang, Y., & Li, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112497 · Cited by 12

A new large model with multi-scale feature fusion for fault diagnosis based on unified time series model

Zhang, Z., Wei, C., Zhang, W., & Wen, L. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113941 · Cited by 11

Comprehensive review of gas turbine fault diagnostic strategies

Soleimani, M., Irani, F. N., Yadegar, M., & Meskin, N. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126801 · Cited by 11

Large language model to assist data augmentation in soft contrastive learning for few-shot machinery fault diagnosis

Yu, Y., Ji, J., Chen, Z., Dhupia, J. S., Tang, L., Liu, Z., & Xiang, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104078 · Cited by 13

A comprehensive review on generative artificial intelligence models for fault diagnostics and prognostics of rotating machineries

Mian, T., & Kundu, P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113594 · Cited by 11

New Era of AI in Chemical Process Safety: Foundation Models

Yang, H., Kareck, T. L., & Wang, Q. (2026) · ACS Chemical Health & Safety · doi:10.1021/acs.chas.5c00227 · Cited by 11

A unified rotating machinery health management framework leveraging large language models for diverse components, conditions, and tasks

Peng, H., Gao, J., Liu, J., Du, J., & Wang, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112544 · Cited by 14

A systematic procedure for the analysis of maintenance reports based on a taxonomy and BERT attention mechanism

Valcamonico, D., Baraldi, P., Macêdo, J. B., Moura, M. D. C., Brown, J., Gauthier, S., & Zio, E. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110834 · Cited by 11

Temporal causal graph-based attention gated recurrent unit for interpretable fault diagnosis in nuclear power plants

Liu, J., Pan, K., Zio, E., & Yao, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112332 · Cited by 9

A unified multimodal large language model for explainable tool wear state assessment and maintenance decision support

Yan, L., Luo, H., Du, S., Wang, S., Li, S., & Chen, X. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113287 · Cited by 9

DeFIANT: A degradation-aware large model paradigm for generalizable RUL prediction

Tao, L., Zhao, Z., Wang, X., Li, B., Zhan, W., Su, X., Wang, X., & Lian, Z. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113236 · Cited by 8

Large Language and Foundation Models for Machinery Health Monitoring: A Systematic Review

Tsallis, C., Papageorgas, P. G., Munteanu, R. A., & Dellagi, S. (2026) · Applied Sciences · doi:10.3390/app16052493 · Cited by 8

A multi-task integrated question-answering framework for highly generalizable fault and RUL assessments based on LLMs with a post-training and fine-tuning strategy

Guo, Z., Yang, B., Xie, J., Wang, T., Yang, J., & Niu, B. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113402 · Cited by 8

Dynamic risk-informed verification prioritization for Complex Product Systems: A tri-metric approach using a Multi-State Hierarchical Bayesian Network

Dong, C., & Yang, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111146 · Cited by 7

Deconfounding enhanced image-based knowledge distillation for fault diagnosis with an application in manufacturing process

Zhang, J., & Liu, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112907 · Cited by 7

Joint state-of-charge and state-of-health estimation of lithium-ion batteries across varying operational stages on differing timescales with large language model: a multi-task prompting method

Bian, C., Duan, Z., Li, D., Yang, S., & Feng, J. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111872 · Cited by 7

Domain-specific large language model-driven risk analysis of battery energy storage systems

Liang, J., Meng, H., & Mu, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112416 · Cited by 7

VSLLaVA: A pipeline for Large Multimodal Foundation Models in industrial vibration signal analysis

Li, Q., Zhang, X., Huang, J., He, H., Zhang, F., Qin, Z., & Chu, F. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.105023 · Cited by 7

Causality-enhanced system reliability and safety analysis: An overview

Zheng, S., Pan, K., Chen, Y., Liu, J., & Zio, E. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.112109 · Cited by 6

Fault inference diagnosis based on causal network and large language model-enhanced knowledge graph network

Guo, Z., Liu, Z., Estupiñán, E., & Cabrera, D. R. (2026) · Chinese Journal of Mechanical Engineering · doi:10.1016/j.cjme.2025.100162 · Cited by 6

Large models for machinery fault diagnosis: Current advances and future directions

Yan, R., Ren, J., Wen, J., Guo, C., Zhao, Z., & Chen, X. (2026) · Chinese Journal of Mechanical Engineering · doi:10.1016/j.cjme.2026.100277 · Cited by 6

Knowledge-Enhanced Industrial Question-Answering Using Large Language Models

Liu, R., Ren, H., Ren, H., Wu, R., Cui, W., Liang, X., Yang, C., & Gui, W. (2025) · Engineering · doi:10.1016/j.eng.2025.07.035 · Cited by 8

Semi-Supervised Prefix Tuning of Large Language Models for Industrial Fault Diagnosis with Big Data

Chen, G., Yuan, J., Yao, J., Luo, Z., Li, J., & Luo, C. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020038 · Cited by 5

Knowledge graph-driven fault diagnosis for aviation equipment: Integrating improved joint extraction with large language model

Li, L., Haruna, A., Ying, W., Noman, K., & Li, Y. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.101039 · Cited by 5

Graph-constrained association rule mining-based energy efficiency diagnosis method for HVAC systems with knowledge-enhanced large language model

Deng, Q., Chen, Z., Liu, Y., Zhu, Z., Xu, J., Jiang, Z., & Chua, K. J. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.114056 · Cited by 5

From physics to machine learning and back: Part II - Learning and observational bias in prognostics and health management (PHM)

Fink, O., Nejjar, I., Sharma, V., Niresi, K. F., Sun, H., Dong, H., Xu, C., Wei, A., Bizzi, A. A. C., Theiler, R., et al. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112376 · Cited by 21

Long causal chain-of-thought for compound fault diagnosis: enhancing hypergraph-based Causal-LLM intervention reasoning

Li, F., Li, X., & Bao, J. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.03.011 · Cited by 5

Using the Large Language Model ChatGPT to Support Decisions in Sustainable Transport

Ziemba, P., & Majewski, F. (2025) · Sustainability · doi:10.3390/su17167520 · Cited by 4

Multi-head memory large language model for predicting degradation trends of gearbox under non-stationary excitation

Yu, X., Ma, Z., Liu, T., Zhang, Z., & Tang, B. (2025) · Measurement · doi:10.1016/j.measurement.2025.119792 · Cited by 4

Integrating Large Language Model and Lightweight Network for Robust Multimodal Fault Diagnosis in Wheeled Robots

Ye, T., Yuan, X., Liu, J., Mei, X., Niu, X., Song, Y. H., & Zhou, F. (2026) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2025.3649690 · Cited by 4

Uncertain Particle Filtering: A New Real-Time State Estimation Method for Failure Prognostics

Liang, J., Shao, Y., Lio, W., Liu, J., & Kang, R. (2025) · Mathematics · doi:10.3390/math13050702 · Cited by 4

Optimization of Large Language Models with Multimodal Data Training for Fault Diagnosis in Wind Turbine Systems

Zhang, Y., Zhao, Z., Zhang, D., & Dai, J. (2025) · International Conference on Power Electronics and Electric Drives (PEED) · doi:10.1109/peed63748.2025.00038 · Cited by 4

GFIA: Generative Fault Image Analysis via vision–language model its application to train bogie transmission system

Zhang, C., Wang, Y., & You, X. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104482 · Cited by 4

Evaluating LLMs and Prompting Strategies for Automated Hardware Diagnosis from Textual User-Reports

Caminha, C., Silva, M. D. L. M., Chaves, I. C., Brito, F. T., Farias, V. A. E., & Machado, J. C. (2025) · Anais do Seminário Integrado de Software e Hardware (SEMISH) · doi:10.5753/semish.2025.8473 · Cited by 3

An ontology-driven bayesian network approach to fault diagnosis and correction in manufacturing

Wilhelm, Y., Reimann, P., Gauchel, W., & Mitschang, B. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-17136-9 · Cited by 3

Vibration smart language model: A study on intelligent operation and maintenance and decision support system based on multimodal data fusion

Yang, Y., Jing, J., Ran, G., Wang, T., Zhang, G., Zhang, Y., Zhao, J., Ma, Y., & Li, X. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114927 · Cited by 3

Causal-enhanced machine learning framework for long-term wind power probabilistic forecasting

Zechen, Y., Bo, S., Wu, Z., Leyang, Z., & Lin, T. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112532 · Cited by 3

Power ops agent: A knowledge and data dual-driven multi-agent framework for substation operation and maintenance

Xiao, Z., Liu, H., Zhou, X., Cong, S., & Ju, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131965 · Cited by 3

A hierarchical imprecise probability approach to reliability assessment of large language models

Aghazadeh-Chakherlou, R., Guo, Q., Khastgir, S., Popov, P. T., Zhang, X., & Zhao, X. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112615 · Cited by 3

A framework for hallucination mitigation in domain-specialized large language models with application to aviation maintenance decision support

Lu, X., Su, X., Feng, J., Zeng, K., Zhang, S., & Zhang, Y. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104762 · Cited by 3

DPDC-ILKM: A multi-agent integrated large knowledge model for intelligent maintenance of industrial swarm robotics

Chen, J., Xu, Y., Tang, J., Xu, X., Yan, R., Yang, Z., & Li, W. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.101044 · Cited by 2

Fault detection for time-variant avionics systems based on a new data-driven time-varying approach

Lü, L., Wang, J., Wang, B., Sun, W., Chen, Y., Sun, K., Feng, Z., & Chen, H. (2026) · Control Engineering Practice · doi:10.1016/j.conengprac.2026.106802 · Cited by 2

An intelligent over-sampling framework with physics-driven guidance for noise robust imbalanced rolling bearing fault diagnosis

Jiao, W., Lin, D., Dong, Z., Sun, J., Xu, W., Chen, X., Liu, S., & Jiang, Y. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114791 · Cited by 2

Failure Event Mining With Fine‐Tuned Large Language Model: Case Study of Analyzing United States Nuclear Power Plant Failure Event Reports

Zhang, S., Sohag, S. R., Xian, M., Sun, S., & Ma, Z. (2026) · Risk Analysis · doi:10.1111/risa.70191 · Cited by 5

Physics-constrained causal structure learning for root cause analysis in nuclear system monitoring

Yao, Y., Liu, J., Yu, J., & Xie, M. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112696 · Cited by 2

LLM-BFFD: Fine-tuning pre-trained large language models for blast furnace fault diagnosis

Wu, P., Su, X., Liu, Y., Chao, D., Gao, J., Zhang, X., Lou, S., & Yang, C. (2026) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2026.109705 · Cited by 2

Physics guided diffusion-based generative framework for bearing fault diagnosis of offshore wind turbine motors under multi-modal coupling and missing data

Teng, Z., Yi, X., & Wang, B. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.126531 · Cited by 2

Healthcare 4.0: A Large Language Model-Based Blockchain Framework for Medical Device Fault Detection and Diagnostics

Alsaif, K., Albeshri, A. A., Khemakhem, M. A., & Eassa, F. E. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160495 · Cited by 2

LLM-YOLOMS: Large Language Model-based Semantic Interpretation and Fault Diagnosis for Wind Turbine ComponentsPreprint

Li, Y., Wang, Y., Li, M., Li, X., & Feng, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5886005 · Cited by 2

USVDD: A one-class outlier detection method with imprecisely- observed data

Liang, J., & Liu, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113065 · Cited by 1

Enhanced Fault Diagnosis Using Large Language Models and Probabilistic Label Fusion

Chen, Q., Yao, S., Wang, P., Shi, J., Qin, W., Li, J., Wang, J., & He, W. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.09.401 · Cited by 1

Enhancing adversarial robustness of industrial fault diagnosis systems via causal inference-guided detection and purification

Zhao, J., & Li, N. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.112054 · Cited by 1

Uncertain Bayesian decision tree: a new method for multi-class fault diagnosis with observation uncertainty

Liang, J., Kang, R., & Liu, J. (2026) · International Journal of General Systems · doi:10.1080/03081079.2026.2617130 · Cited by 1

Natural-language-processing and safety-engineering-based fault identification technique for electrochemical ESSs

Li, Y., Mei, W., Cheng, Z., Tang, L., Zhuo, P., Kong, D., Hu, Z., ZHANG, Y., Li, C., ZHANG, Y., et al. (2026) · The Innovation · doi:10.1016/j.xinn.2026.101277 · Cited by 1

Signal LLM: Fault perception and maintenance system for dual-drive hydraulic turbine bearings based on cross-attention-LLM

Zeng, Y., Wang, H., Ran, G., Dong, Z., Li, X., Liu, X., & He, J. (2026) · Structural Health Monitoring · doi:10.1177/14759217261422123 · Cited by 1

Towards intelligent safety assessment under blast loading: A large language model-powered multi-agent framework

Wang, L., Yan, J., Yan, J., & Liu, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112560 · Cited by 2

XFD-LVLM: An explainable multimodal framework for aviation hydraulic pump intelligent fault diagnosis with large Vision-Language models

Xu, Q., Lei, Z., Wen, G., Gu, S., Su, Y., Zhao, Z., & Chen, X. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104726 · Cited by 1

ComfortLLM: Compatible Multimodality Fusion-Oriented Large-Language Model for Industrial Fault Diagnosis With Diverse Data

Hu, T., Mo, Z., Zhang, C. L., & Zhang, Z. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3704673 · Cited by 1

A retrieval-augmented, domain-intelligent agentic framework for reliable decision support in safety-critical nuclear engineering

Ndum, Z. N., Tao, J., Ford, J., Yim, M., & Liu, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113057 · Cited by 1

Large language models for fault diagnosis in smart manufacturing: A comprehensive review

Nguyen, D.-T., & Anh, L. H. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200714 · Cited by 1

An Innovative Foundation Model for Bearing Prognostics and Health Management Through Pre-Trained Large Language ModelsPreprint

Wang, W., & Wang, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5127433

Research on Anomaly Sound Detection Methods Based on Large Models

Hu, S., Guo, G., & Zhuo, J. (2025) · International Conference on Control Engineering and Artificial Intelligence (CCEAI) · doi:10.1145/3722150.3722153

LAISA: Large-language Agent for Industrial State Assessment

Li, J., Liu, R., Yang, Y., Li, D., & Yang, Q. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137321

A Multi-stage Language Model Post-Training Framework for HVAC Fault Diagnosis in Urban Rail TransitPreprint

Zhang, W., Fang, H., Wu, T., Wang, C., Xu, L., Bu, J., Yu, Y., & Zhong, Q. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5616733

DiagLLM: A CNN-Language Model Framework with LoRA for Fault Diagnosis of Cross Roller Bearings

Liu, J., Chen, J., Deng, S., Wen, K., He, G., & Li, W. (2025) · International Conference on Equipment Intelligent Operation and Maintenance (ICEIOM) · doi:10.1109/iceiom65271.2025.11239661

A LLM-Driven Agent System for Automotive Fault Diagnosis with Integrated Reasoning and Expertise Sharing

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