由大型语言模型驱动的光谱可解释轴承故障诊断框架
Panfeng Bao1,2, Wenjun Yi1, Yue Zhu2
1National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究引入了一个可解释的故障诊断框架,使用光谱分析和大型语言模型 (LLM). 它以透明的推理提供准确的诊断,增强工业用户的信任和可访问性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 现有的故障诊断方法往往充当黑子,缺乏透明的推理.
- 这限制了用户对工业环境中的诊断结果的信任和理解.
研究的目的:
- 开发一种新的,可解释的故障诊断框架.
- 将光谱特征提取与大型语言模型 (LLM) 整合起来,以实现透明的诊断推理.
主要方法:
- 使用基于希尔伯特和富里埃的编码器将振动信号转换为光谱表示.
- 频道注意力增强卷积神经网络 (CNN) 用于初始故障预测.
- 精心调整的LLM整合了光谱特征和CNN输出,用于诊断和推理.
主要成果:
- 该框架实现了高诊断性能.
- 实质上提高了故障诊断的解释性.
- 通过透明的推理证明了准确的诊断.
结论:
- 拟议的框架增强了对故障诊断的信任和理解.
- 为非专业的工业用户提供先进的故障诊断.
- 为黑盒诊断模型提供了一个透明而准确的替代方案.
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