Research on General Foundation Model for Intelligent Fault Diagnosis for Rotating Machinery

LI Xiang; XU Yixiao; LEI Yaguo; LI Xiwei; LI Naipeng; YANG Bin

doi:10.7652/xjtuxb202507001

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Research on General Foundation Model for Intelligent Fault Diagnosis for Rotating Machinery

Special Topic　Big Data Intelligent Fault Diagnosis and Prediction | 更新时间：2025-07-09

- Research on General Foundation Model for Intelligent Fault Diagnosis for Rotating Machinery
  增强出版
- Journal of Xi’an Jiaotong University Vol. 59, Issue 7, Pages: 1-12(2025)
- 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：
- 基金信息：
- DOI：10.7652/xjtuxb202507001
  CLC： TH17
- Received：30 September 2024，
  
  Online First：02 January 2025，
  
  Published：10 July 2025
- 稿件说明：
移动端阅览
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：

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

LEI Yaguo , YANG Bin , JIANG Xinwei , et al . Applications of machine learning to machine fault diagnosis: a review and roadmap [J ] . Mechanical Systems and Signal Processing , 2020 , 138 : 106587 .

YU Yaoxiang , GUO Liang , GAO Hongli , et al . FedCAE: a new federated learning framework for edge-cloud collaboration based machine fault diagnosis [J ] . IEEE Transactions on Industrial Electronics , 2024 , 71 ( 4 ): 4108 - 4119 .

雷亚国 , 杨彬 , 李乃鹏 , 等 . 跨设备的机械故障靶向迁移诊断方法 [J ] . 机械工程学报 , 2022 , 58 ( 12 ): 1 - 9 .

LEI Yaguo , YANG Bin , LI Naipeng , et al . Targeted transfer diagnosis method across different machines [J ] . Journal of Mechanical Engineering , 2022 , 58 ( 12 ): 1 - 9 .

LI Weihua , HUANG Ruyi , LI Jipu , et al . A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: theories, applications and challenges [J ] . Mechanical Systems and Signal Processing , 2022 , 167 ( Part A ): 108487 .

CHEN Zhuyun , HE Guolin , LI Jipu , et al . Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery [J ] . IEEE Transactions on Instrumentation and Measurement , 2020 , 69 ( 11 ): 8702 - 8712 .

ZHAO Ke , JIA Feng , SHAO Haidong . A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains [J ] . Knowledge-Based Systems , 2023 , 262 : 110203 .

WU Tianyu , HE Shizhu , LIU Jingping , et al . A brief overview of ChatGPT: the history, status quo and potential future development [J ] . IEEE/CAA Journal of Automatica Sinica , 2023 , 10 ( 5 ): 1122 - 1136 .

LI Xuan , MIAO Qinghai , LI Lingxi , et al . Sora for scenarios engineering of intelligent vehicles: V&V, C&C, and beyonds [J ] . IEEE Transactions on Intelligent Vehicles , 2024 , 9 ( 2 ): 3117 - 3122 .

WEN Licheng , YANG Xuemeng , FU Daocheng , et al . On the road with GPT-4V(ision): explorations of utilizing visual-language model as autonomous driving agent [C ] // ICLR 2024 Workshop on Large Language Model (LLM) Agents . New York, USA : ICLR , 2024 : 1 - 31 .

ANIL R , BORGEAUD S , ALAYRAC J B , et al . Gemini: a family of highly capable multimodal models [EB/OL ] . ( 2024-06-17 ) [ 2024-09-01 ] . https://arxiv.org/abs/2312.11805 https://arxiv.org/abs/2312.11805 .

LI Yanfu , WANG Huan , SUN Muxia . ChatGPT-like large-scale foundation models for prognostics and health management: a survey and roadmaps [J ] . Reliability Engineering & System Safety , 2024 , 243 : 109850 .

ZHANG Haotian , SEMUJJU S D , WANG Zhicheng , et al . Large scale foundation models for intelligent manufacturing applications: a survey [J/OL ] . Journal of Intelligent Manufacturing . ( 2025-01-04 ) [ 2025-01-10 ] . https://doi.org/10.1007/s10845-024-02536-7 https://doi.org/10.1007/s10845-024-02536-7 .

陈祝云 , 钟琪 , 黄如意 , 等 . 基于增强迁移卷积神经网络的机械智能故障诊断 [J ] . 机械工程学报 , 2021 , 57 ( 21 ): 96 - 105 .

CHEN Zhuyun , ZHONG Qi , HUANG Ruyi , et al . Intelligent fault diagnosis for machinery based on enhanced transfer convolutional neural network [J ] . Journal of Mechanical Engineering , 2021 , 57 ( 21 ): 96 - 105 .

卞文彬 , 邓艾东 , 刘东川 , 等 . 基于改进深度残差收缩网络的风电机组滚动轴承故障诊断方法 [J ] . 机械工程学报 , 2023 , 59 ( 12 ): 202 - 214 .

BIAN Wenbin , DENG Aidong , LIU Dongchuan , et al . Fault diagnosis method of wind turbine rolling bearing based on improved deep residual shrinkage network [J ] . Journal of Mechanical Engineering , 2023 , 59 ( 12 ): 202 - 214 .

王志颖 , 李天福 , 许文纲 , 等 . 降噪混合注意力变分自编码器及其在轴向柱塞泵故障诊断中的应用 [J ] . 机械工程学报 , 2024 , 60 ( 4 ): 167 - 177 .

WANG Zhiying , LI Tianfu , XU Wengang , et al . Denoising mixed attention variational auto-encoder for axial piston pump fault diagnosis [J ] . Journal of Mechanical Engineering , 2024 , 60 ( 4 ): 167 - 177 .

康涛 , 段蓉凯 , 杨磊 , 等 . 融合多注意力机制的卷积神经网络轴承故障诊断方法 [J ] . 西安交通大学学报 , 2022 , 56 ( 12 ): 68 - 77 .

KANG Tao , DUAN Rongkai , YANG Lei , et al . Bearing fault diagnosis using convolutional neural network based on a multi-attention mechanism [J ] . Journal of Xi'an Jiaotong University , 2022 , 56 ( 12 ): 68 - 77 .

贾峰 , 李世豪 , 沈建军 , 等 . 采用深度迁移学习与自适应加权的滚动轴承故障诊断 [J ] . 西安交通大学学报 , 2022 , 56 ( 8 ): 1 - 10 .

JIA Feng , LI Shihao , SHEN Jianjun , et al . Fault diagnosis of rolling bearings using deep transfer learning and adaptive weighting [J ] . Journal of Xi'an Jiaotong University , 2022 , 56 ( 8 ): 1 - 10 .

DING Yifei , JIA Minping , MIAO Qiuhua , et al . A novel time-frequency transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings [J ] . Mechanical Systems and Signal Processing , 2022 , 168 : 108616 .

ZHANG Yongchao , JI J C , REN Zhaohui , et al . Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing [J ] . Reliability Engineering & System Safety , 2023 , 234 : 109186 .

SMITH W A , RANDALL R B . Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study [J ] . Mechanical Systems and Signal Processing , 2015 , 64-65 : 100 - 131 .

LESSMEIER C , KIMOTHO J K , ZIMMER D , et al . Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification [C ] // PHM Society European Conference . New York, USA : PHM , 2016 : 1577 .

ZHAO Chao , ZIO E , SHEN Weiming . Domain generalization for cross-domain fault diagnosis: an application-oriented perspective and a benchmark study [J ] . Reliability Engineering & System Safety , 2024 , 245 : 109964 .

CHEN Mingzhi , SHAO Haidong , DOU Haoxuan , et al . Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples [J ] . IEEE Transactions on Reliability , 2023 , 72 ( 3 ): 1029 - 1037 .

DAGA A P , FASANA A , MARCHESIELLO S , et al . The Politecnico di Torino rolling bearing test rig: description and analysis of open access data [J ] . Mechanical Systems and Signal Processing , 2019 , 120 : 252 - 273 .

雷亚国 , 韩天宇 , 王彪 , 等 . XJTU-SY滚动轴承加速寿命试验数据集解读 [J ] . 机械工程学报 , 2019 , 55 ( 16 ): 1 - 6 .

LEI Yaguo , HAN Tianyu , WANG Biao , et al . XJTU-SY rolling element bearing accelerated life test datasets: a tutorial [J ] . Journal of Mechanical Engineering , 2019 , 55 ( 16 ): 1 - 6 .

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