机械故障诊断中的预后分析的大型语言模型
Hao Zhang1, Wei Wang2, Longfei Zhang2
1The graduate school of Air Force Engineering University, Xi'an, China.
PloS one
|November 21, 2025
概括
本研究引入了使用大型语言模型进行旋转机械故障诊断的新框架. 它将多模式数据融合在一起,以改善特征提取,并生成可解释的报告,以改善健康管理.
科学领域:
- 工业情报 工业情报 工业情报
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 旋转机械对于复杂的系统至关重要,需要强大的故障诊断和健康管理.
- 传统的振动信号分析在复杂系统中面临局限性,难以进行全面的故障特征提取和概括.
研究的目的:
- 提出一个智能诊断框架,利用旋转机械的大型语言模型 (LLM).
- 通过增强特征提取和跨场景概括,克服传统方法的局限性.
主要方法:
- 开发了一个基于LLM的框架,集成多式联运数据:原始振动信号,时间频谱特征和故障知识文本.
- 使用特征融合用于机械故障特征的交叉模式联合表示.
- 综合原则知识库,以提高诊断能力.
主要成果:
- 与传统方法相比,该框架在故障诊断和健康管理方面表现优异.
- 在轴承数据集上在不同的工业场景中实现了出色的性能和适应性.
- 该模型成功输出故障位置,原因分析和维护策略建议.
结论:
- 拟议的基于LLM的框架在旋转机械故障诊断和健康管理方面取得了重大进展.
- 多模式数据融合和LLM集成有效地解决了传统信号分析的局限性.
- 该框架提供可解释的报告,增强在工业环境中的实际应用性.
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