BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation

TL;DR

BearLLM introduces a multimodal framework that unifies bearing health management tasks by combining vibration signals and language models, achieving state-of-the-art fault diagnosis performance across multiple datasets.

Contribution

The paper presents a novel multimodal model, BearLLM, with a unified vibration signal representation and a large-scale dataset, advancing industrial fault diagnosis capabilities.

Findings

Achieves state-of-the-art performance on nine benchmarks.

Unifies multiple bearing tasks into a single multimodal framework.

Provides a large-scale multimodal bearing health dataset.

Abstract

We propose a bearing health management framework leveraging large language models (BearLLM), a novel multimodal model that unifies multiple bearing-related tasks by processing user prompts and vibration signals. Specifically, we introduce a prior knowledge-enhanced unified vibration signal representation to handle various working conditions across multiple datasets. This involves adaptively sampling the vibration signals based on the sampling rate of the sensor, incorporating the frequency domain to unify input dimensions, and using a fault-free reference signal as an auxiliary input. To extract features from vibration signals, we first train a fault classification network, then convert and align the extracted features into word embedding, and finally concatenate these with text embedding as input to an LLM. To evaluate the performance of the proposed method, we constructed the first large-scale multimodal bearing health management (MBHM) dataset, including paired vibration signals and textual descriptions. With our unified vibration signal representation, BearLLM using one set of pre-trained weights achieves state-of-the-art performance on nine publicly available fault diagnosis benchmarks, outperforming specific methods designed for individual datasets. We provide a dataset, our model, and code to inspire future research on building more capable industrial multimodal models https://github.com/SIA-IDE/BearLLM.