Theses and Dissertations
ORCID
https://orcid.org/0009-0006-2385-4385
Advisor
Rahimi, Shahram
Committee Member
Tian, Wenmeng
Committee Member
Sescu, Adrian
Committee Member
Amirlatifi, Amin
Date of Degree
5-16-2025
Original embargo terms
Immediate Worldwide Access
Document Type
Dissertation - Open Access
Major
Computational Engineering
Degree Name
Doctor of Philosophy (Ph.D.)
College
James Worth Bagley College of Engineering
Department
Computational Engineering Program
Abstract
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies that are adaptive, transferable, and capable of integrating domain-specific knowledge. This work presents RAAD-LLM, a novel framework for adaptive anomaly detection, leveraging large language models (LLMs) integrated with Retrieval-Augmented Generation (RAG). This approach addresses the aforementioned PdM challenges. By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time-series data without requiring fine-tuning on specific datasets. The framework's adaptability mechanism enables it to adjust its understanding of normal operating conditions dynamically, thus increasing detection accuracy. This methodology is validated on a real-world dataset provided by a plastics manufacturing plant and the Skoltech Anomaly Benchmark (SKAB) dataset. Results show significant improvements over the previous model with an accuracy increase from 70.7% to 88.6% on the real-world dataset. By allowing for the enriching of input series data with semantics, RAAD-LLM incorporates multimodal capabilities that facilitate more collaborative decision-making between the model and plant operators. Overall, findings support RAAD-LLM's ability to revolutionize anomaly detection methodologies in PdM, potentially leading to a paradigm shift in how anomaly detection is implemented across various industries.
Sponsorship (Optional)
W912HZ23C0013
Recommended Citation
Russell-Gilbert, Alicia, "RAAD-LLM: adaptive anomaly detection using LLMs and RAG integration" (2025). Theses and Dissertations. 6567.
https://scholarsjunction.msstate.edu/td/6567
