Summary of Fd-llm: Large Language Model For Fault Diagnosis Of Machines, by Hamzah A.a.m. Qaid et al.
FD-LLM: Large Language Model for Fault Diagnosis of Machines
by Hamzah A.A.M. Qaid, Bo Zhang, Dan Li, See-Kiong Ng, Wei Li
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study introduces a novel approach to Intelligent Fault Diagnosis (IFD), adapting Large Language Models (LLMs) to numerical data inputs. The proposed FD-LLM framework formulates the training of LLMs as a multi-class classification problem, effectively capturing complex sensor data information from vibration signals, temperature readings, and operational metrics. Two encoding methods are explored: string-based tokenization for text representations and statistical feature extraction from time-frequency domains. Four open-sourced LLMs (Llama3 and Llama3-instruct) are assessed based on the FD-LLM framework, demonstrating strong fault detection capabilities and adaptability across different operational conditions, outperforming state-of-the-art deep learning approaches in many cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research combines big language models with sensor data to improve machine fault diagnosis. Traditional text-based models struggle to capture important information from sensors like vibration signals and temperature readings. The new approach, called FD-LLM, is better at recognizing different types of faults by learning patterns in the sensor data. Two ways are tested to turn sensor data into a format that the language model can understand: one uses words to represent the data, while the other extracts important statistics from the data. Four different big language models are tried and show strong performance in detecting machine faults, even when conditions change. |
Keywords
» Artificial intelligence » Classification » Deep learning » Feature extraction » Language model » Temperature » Tokenization
