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

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

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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Learning without Forgetting

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Large language models encode clinical knowledge

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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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Domain-Adaptive LLM Framework for Industrial Equipment Maintenance: Integrating LoRA and Retrieval-Augmented Generation

Gao, R. (2026) · International Conference on Computer, Artificial Intelligence and Control Engineering (CAICE) · doi:10.1145/3804601.3804798

An Equipment Health Optimization Method for Gas Gathering Stations Integrating Topology‐Constrained Mamba and Memory AgentPreprint

jing, X., cao, Q., luo, J., & cheng, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6883279

Harnessing AI-driven large language models (LLMs) for enhanced forecasting and optimisation of wind and solar energy generation and load demand profiles

Aleassa, O., Bekhit, M., Hussain, W., & Fattah, I. M. R. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.117171

Causal network construction and health state assessment of electromechanical system based on normalized transfer entropy

Dong, K., Xu, D., Li, S., & Xiao, X. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113036

Research on wind turbine fault root cause localization method integrating large language models and causal hypergraphs

Zhang, Y. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.111335

An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

Wang, J., Cheng, F., Wu, W., Li, H., Peng, G., & Liu, T. (2026) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim65483.2026.11658248

Aircraft Engine Fault Detection Based on Fine-Tuned Large Language Models

Du, J., & Du, W. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11559751

A Large Language Model‐Based Approach for Fault Detection and Its Application

Ye, Y., Zhu, Y., Che, L., & Zhou, H. (2026) · Safety Science and Technology · doi:10.1002/sst3.70028

A human-centric decision support framework for edge-based cold chain transportation monitoring with Small Language Models

Li, H., Uygun, Ö., Yu, X., Zhou, Y., Yang, Z., & Chen, C. (2026) · Computers & Industrial Engineering · doi:10.1016/j.cie.2026.112240

From detection to decision: a structured reasoning framework for industrial fault diagnosis

Li, Y., Wang, Y., Li, X., Wan, G., & Li, M. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae8cb5

A Lightweight Bayesian Network Framework for Risk Analysis of Hydrogen-Blended Pipelines With Large Language Model Integration

Chen, Y., Qian, K., & He, R. (2026) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B Mechanical Engineering · doi:10.1115/1.4072425

Enhancing LLM-based generation of safety hazard rectification measures for mega hydropower construction via cascaded RAG and adaptive ColBERT

Lv, Y., Li, M., Li, M., Yu, Y., Liu, L., & Zhang, X. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.113235

A State-Of-The-Art Review of Industrial Time Series Data Analysis: Methods and Applications

Liu, L., Zhang, Y., Sun, Y., Cai, H., & Wang, C. (2026) · ACM Computing Surveys · doi:10.1145/3844497

A Text-Driven, Human-Centric Framework for Sustainable Safety Risk Governance in Steel Manufacturing: Integrating NLP, Topic Modeling, and Bayesian Decision Fusion

Li, J., Wang, Z., Wang, Y., Sun, X., Li, Y., Ding, H., & Xu, Y. (2026) · Sustainability · doi:10.3390/su18189227

Knowledge Graph-Enhanced Causal Reasoning and RAG for PLC Control System Fault Diagnosis

Huang, L., Cui, Q., Yu, L., & Dai, J. (2026) · International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) · doi:10.1109/aiotc71250.2026.11688615

A Multi-Stage Post-Training Framework for Domain-Specific Language Models in Fault Diagnosis

Zhang, W., Fang, H., Wu, T., Wang, C., Xu, L., Bu, J., Yu, Y., & Zhong, Q. (2026) · AI · doi:10.3390/ai7090383

HIC-LLM: Health-Indicator Consistency Scoring and Optional Evidence-Grounded Large Language Model Reporting

Xing, Y., Zhang, Q., Li, S., Li, K., Yao, Z., Zhao, J., & Wang, Z. (2026) · Electronics · doi:10.3390/electronics15194360

Auditable progressive multimodal bearing fault diagnosis with Large language model Screening

Wang, J., Peng, G., Chen, Y., Zhāng, W., Wu, W., & Liu, T. (2026) · Measurement · doi:10.1016/j.measurement.2026.123183

A Holistic Comparative Study of AI Models for Reliable Household Energy Consumption Forecasting

Guo, X., & Xing, L. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3736727

Optimal Control of RLC Circuits Under Singular Excitations: A Generalized Jump Operator with Neural and LLM Approximations

Mputu, E. K., Ngoie, R.-B. M., Linguma, D. N., Nzambi, C. L., Kinyoka, G. K., Kasereka, S. K., & Bukweli, J.-D. K. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.08.456

Process monitoring of acid gas removal unit using deep forecasting and LLM-based interpretation

Khan, N., Taqvi, S. A. A., Waqas, M., & Naqvi, S. R. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.113201

Policy-Guarded Explainable ML–LLM Framework for Data-Driven Control in Digital Twin Surrogate

Gautam, A., Aryal, M. R., & Anand, S. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.08.051

Ethical and governance challenges of large language models in power and energy systems: a review and framework for responsible AI deployment

Sulaima, M. F., Arora, S., Krejcar, O., Herout, L., & Namazi, H. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100869

Liquid-propellant rocket engine health monitoring technologies: A survey, insights, and prospects

Wu, J., Liu, Y., Cheng, Y., Shuming, Y., & Li, Y. (2026) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2026.101251

Risk-Aware Predictive Maintenance for Aero-Engines via CVaR-Guided Reinforcement Learning and LLM-Based Risk Abstraction

Song, B., Guo, Y., Yu, Z., Ni, Y., & Wu, W. (2026) · Sensors · doi:10.3390/s26206473

A Fine-Tuned Large Time-Series Model for Soft Sensing in Industrial Processes

Chen, J., Yang, Z., Bi, Z., Jiang, X., & Chen, G. (2026) · Learning and analytics in intelligent systems · doi:10.1007/978-3-032-26620-0_12

AI-Driven Alarm Management in Industrial Systems: Methods and Applications

Liu, C., Peng, W., & Yu, X. (2026) · ENGINEERING Artificial Intelligence · doi:10.2738/engai.2026.0003

PHM-LLM: A cross-modal pre-trained LLM for bearing fault diagnosis and remaining useful life prediction

Wang, W., Wang, D., Wang, R., & Song, B. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135551

Exploring LLM-based Frameworks for Fault DiagnosisPreprint

Lee, X. Y., Vidyaratne, L., Farahat, A., & Gupta, C. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2509.23113

The Interpretable Reasoning and Intelligent Decision-Making Based on Event Knowledge Graph With LLMs in Fault Diagnosis Scenarios

Men, C., Han, Y., Wang, P., Tao, J., & Huang, C. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3550999 · Cited by 37

Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning

Zhang, J., Zhang, C., Lu, J., & Zhao, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124378 · Cited by 79

Using SLMs and LLMs for Diagnosis

Plank, S., & Wotawa, F. (2026) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/oasics.dx.2026.14

A Multi-stage Post-Training Framework for Domain-Specific Language Models in Fault DiagnosisPreprint

Zhang, W., Fang, H., Wu, T., Wang, C., Xu, L., Bu, J., Yu, Y. S., & Zhong, Q. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5953702