FD-LLM: Large Language Model for Fault Diagnosis of Machines

TL;DR

This paper introduces FD-LLM, a novel framework adapting large language models to numerical sensor data for machine fault diagnosis, demonstrating superior performance and adaptability over traditional deep learning methods.

Contribution

The study develops FD-LLM, a new approach that encodes sensor data into text for LLM-based fault diagnosis, enhancing flexibility and accuracy in various operational scenarios.

Findings

LLMs like Llama3 outperform traditional DL methods in fault detection.

FD-LLM shows high adaptability across different machine conditions.

Encoding methods effectively translate sensor data for LLM input.

Abstract

Large language models (LLMs) are effective at capturing complex, valuable conceptual representations from textual data for a wide range of real-world applications. However, in fields like Intelligent Fault Diagnosis (IFD), incorporating additional sensor data-such as vibration signals, temperature readings, and operational metrics-is essential but it is challenging to capture such sensor data information within traditional text corpora. This study introduces a novel IFD approach by effectively adapting LLMs to numerical data inputs for identifying various machine faults from time-series sensor data. We propose FD-LLM, an LLM framework specifically designed for fault diagnosis by formulating the training of the LLM as a multi-class classification problem. We explore two methods for encoding vibration signals: the first method uses a string-based tokenization technique to encode vibration signals into text representations, while the second extracts statistical features from both the time and frequency domains as statistical summaries of each signal. We assess the fault diagnosis capabilities of four open-sourced LLMs based on the FD-LLM framework, and evaluate the models' adaptability and generalizability under various operational conditions and machine components, namely for traditional fault diagnosis, cross-operational conditions, and cross-machine component settings. Our results show that LLMs such as Llama3 and Llama3-instruct demonstrate strong fault detection capabilities and significant adaptability across different operational conditions, outperforming state-of-the-art deep learning (DL) approaches in many cases.