面向旋转机械装备的智能故障诊断通用基础模型研究

李响; 徐宜销; 雷亚国; 李熹伟; 李乃鹏; 杨彬

doi:10.7652/xjtuxb202507001

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面向旋转机械装备的智能故障诊断通用基础模型研究

专题　大数据智能故障诊断与预测 | 更新时间：2025-07-09

- 面向旋转机械装备的智能故障诊断通用基础模型研究
  增强出版
- Research on General Foundation Model for Intelligent Fault Diagnosis for Rotating Machinery
- 西安交通大学学报 2025年59卷第7期页码：1-12
- 作者机构：
  
  西安交通大学现代设计及转子轴承系统教育部重点实验室， 710049，西安
- 作者简介：
  
  李响（1990—），男，教授，博士生导师；
  雷亚国，男，教授，博士生导师。
- 基金信息：
  
  国家重点研发计划资助项目(2022YFB3402100);国家自然科学基金资助项目(52475125;52005086)
- DOI：10.7652/xjtuxb202507001
  中图分类号： TH17
- 收稿：2024-09-30，
  
  网络首发：2025-01-02，
  
  纸质出版：2025-07-10
- 稿件说明：
移动端阅览
李响, 徐宜销, 雷亚国, 等. 面向旋转机械装备的智能故障诊断通用基础模型研究[J]. 西安交通大学学报, 2025,59(7):1-12.

LI Xiang, XU Yixiao, LEI Yaguo, et al. Research on General Foundation Model for Intelligent Fault Diagnosis for Rotating Machinery[J]. Journal of Xi’an Jiaotong University, 2025, 59(7): 1-12.
李响, 徐宜销, 雷亚国, 等. 面向旋转机械装备的智能故障诊断通用基础模型研究[J]. 西安交通大学学报, 2025,59(7):1-12. DOI： 10.7652/xjtuxb202507001.

LI Xiang, XU Yixiao, LEI Yaguo, et al. Research on General Foundation Model for Intelligent Fault Diagnosis for Rotating Machinery[J]. Journal of Xi’an Jiaotong University, 2025, 59(7): 1-12. DOI： 10.7652/xjtuxb202507001.

摘要

针对现有旋转机械智能故障诊断方法普适性较差，通常仅适用于特定型号、结构、工况、测点、载荷等场景的问题，建立了面向旋转机械装备的智能故障诊断通用基础模型。通过挖掘多种类型旋转机械装备海量状态监测数据，提出了多源数据结构多尺度自适应对齐方法；构建了多层级状态融合的智能诊断模型；建立了对典型旋转机械装备普适性强的智能故障诊断通用基础模型，并提出了诊断通用基础模型的个体定制化适配方法。在广泛使用的旋转机械装备状态监测数据集上对所提方法进行了验证。实验结果表明：所提智能诊断通用基础模型能够直接对未知实测装备进行异常检测，在未进行监督微调的情况下，模型整体诊断准确率达到了88.5%；在少量数据微调下，能够迅速适应实测装备，实现高达98.6%的诊断准确率。此外，所提数据预处理方法在实现跨设备健康状态信号幅值尺度归一化的同时，可保持同一设备内健康与故障状态信号间的幅值相对分布不变，有效保留了关键幅值特征差异。所提出的方法具有良好的工程应用潜力，有望在工程场景下推广应用。

Abstract

Given that existing intelligent fault diagnosis methods for rotating machinery often lack generalizability and are typically limited to specific models

structures

operating conditions

measurement points

and load scenarios

a universal fundamental model for intelligent fault diagnosis tailored to rotating machinery is developed. By mining massive volumes of state monitoring data from various types of rotating machinery

a multi-source data structure with a multi-scale adaptive alignment method is proposed. A multi-level state fusion intelligent diagnosis model is constructed

and a universal fundamental model with strong applicability to typical rotating machinery is established. Additionally

a method for individualized customization and adaptation of the diagnosis model is introduced. The proposed method is validated on extensive state monitoring datasets for rotating machinery. Experimental results show that the universal intelligent diagnosis model can directly detect anomalies in unknown measured equipment

achieving an overall diagnosis accuracy of 88.5% without any supervised fine-tuning. With minor fine-tuning using a small amount of measured data

the model rapidly adapts to new equipment and achieves a diagnosis accuracy of up to 98.6%. Furthermore

the proposed data preprocessing method enables cross-equipment signal amplitude normalization while preserving the relative amplitude distribution between healthy and faulty states within the same equipment

effectively retaining key amplitude-based feature differences. These findings demonstrate the strong engineering potential of the proposed method and its promise for widespread application in real-world industrial scenarios.

关键词

Keywords

references

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