mlsanityData to 14 Oct 2026

Prescriptive Agents based on RAG for Automated Maintenance (PARAM)

arXiv 2508.04714 ↗First posted 28 Jul 2025cs.AI cs.CL cs.LGcited by 2

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Industrial machinery maintenance requires timely intervention to prevent catastrophic failures and optimize operational efficiency. This paper presents an integrated Large Language Model (LLM)-based intelligent system for prescriptive maintenance that extends beyond traditional anomaly detection to provide actionable maintenance recommendations. Building upon our prior LAMP framework for numerical data analysis, we develop a comprehensive solution that combines bearing vibration frequency analysis with multi agentic generation for intelligent maintenance planning. Our approach serializes bearing vibration data (BPFO, BPFI, BSF, FTF frequencies) into natural language for LLM processing, enabling few-shot anomaly detection with high accuracy. The system classifies fault types (inner race, outer race, ball/roller, cage faults) and assesses severity levels. A multi-agentic component processes maintenance manuals using vector embeddings and semantic search, while also conducting web searches to retrieve comprehensive procedural knowledge and access up-to-date maintenance practices for more accurate and in-depth recommendations. The Gemini model then generates structured maintenance recommendations includes immediate actions, inspection checklists, corrective measures, parts requirements, and timeline specifications. Experimental validation in bearing vibration datasets demonstrates effective anomaly detection and contextually relevant maintenance guidance. The system successfully bridges the gap between condition monitoring and actionable maintenance planning, providing industrial practitioners with intelligent decision support. This work advances the application of LLMs in industrial maintenance, offering a scalable framework for prescriptive maintenance across machinery components and industrial sectors.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 12 of its 33 references that are in the map. First posted on arXiv 28 Jul 2025.

Builds on

12 of 33 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
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  7. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
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  10. 2017Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathologycited by 13
  11. 2024Introducing a new hyper-parameter for RAG: Context Window Utilizationcited by 8
  12. 2025KnowsLM: A framework for evaluation of small language models for knowledge augmentation and humanised conversationscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answeringcited by 2
  2. 2026Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Agent-based Condition Monitoring Assistance with Multimodal Industrial Database Retrieval Augmented Generation
  2. 2025Cleaning Maintenance Logs with LLM Agents for Improved Predictive Maintenance
  3. 2026Can LLM Agents Efficiently Identify Appliance Faults? Insights From Diagnosing a Simulated Appliance
  4. 2024LLM-R: A Framework for Domain-Adaptive Maintenance Scheme Generation Combining Hierarchical Agents and RAG
  5. 2026Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data
  6. 2025CAMB: A comprehensive industrial LLM benchmark on civil aviation maintenance
  7. 2025Hybrid Agentic AI and Multi-Agent Systems in Smart Manufacturing
  8. 2025Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?