数字孪生驱动的小样本旋转机械剩余寿命预测

张诚; 马梓玮; 刘斌; 孙铮; 徐俊

doi:10.7652/xjtuxb202312017

您当前的位置：

首页 >

文章列表页 >

数字孪生驱动的小样本旋转机械剩余寿命预测

—————————————————————— | 更新时间：2025-07-09

- 数字孪生驱动的小样本旋转机械剩余寿命预测
- Digital Twin Driven Few-Shot Prediction of Remaining Useful Life for Rotating Machinery
- 西安交通大学学报 2023年57卷第12期页码：168-178
- 作者机构：
  
  1. 巴斯夫新材料有限公司研发部,上海,200137
  2. 西安交通大学未来技术学院,西安,710049
  3. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
  
  : 2023-05-10。作者简介: 张诚(1992—),男,工程师
  孙铮(通信作者),男,副研究员,硕士生导师。基金项目: 国家重点研发计划资助项目(2020YFB1708400)。
- 基金信息：
  
  : 2023-05-10。作者简介: 张诚(1992—),男,工程师;孙铮(通信作者),男,副研究员,硕士生导师。基金项目: 国家重点研发计划资助项目(2020YFB1708400)。
- DOI：10.7652/xjtuxb202312017
  中图分类号： TH17;TP29
- 网络首发：2023-12-10，
  
  纸质出版：2023
- 稿件说明：
移动端阅览
张诚, 马梓玮, 刘斌, 等. 数字孪生驱动的小样本旋转机械剩余寿命预测[J]. 西安交通大学学报, 2023,57(12):168-178.

ZHANG Cheng, MA Ziwei, LIU Bin, et al. Digital Twin Driven Few-Shot Prediction of Remaining Useful Life for Rotating Machinery[J]. 2023, 57(12): 168-178.
张诚, 马梓玮, 刘斌, 等. 数字孪生驱动的小样本旋转机械剩余寿命预测[J]. 西安交通大学学报, 2023,57(12):168-178. DOI： 10.7652/xjtuxb202312017.

ZHANG Cheng, MA Ziwei, LIU Bin, et al. Digital Twin Driven Few-Shot Prediction of Remaining Useful Life for Rotating Machinery[J]. 2023, 57(12): 168-178. DOI： 10.7652/xjtuxb202312017.

摘要

针对小样本数据下旋转机械剩余使用寿命难以准确预测的问题

提出一种数字孪生驱动的旋转机械健康因子构建及剩余使用寿命预测方法。通过融合卷积自编码器与Weibull分布构建数据驱动的旋转机械退化行为模型。使用旋转机械早期退化信号训练卷积自编码器

使其学习到早期退化信号模式; 使用卷积自编码器对测试信号进行重构并计算重构误差

将重构误差映射到[0

1]区间作为健康因子

根据健康因子拟合Weibull可靠度函数并预测旋转机械剩余使用寿命; 在旋转机械持续运行过程中基于实时数据重复上述步骤

实现旋转机械剩余寿命的实时更新。在PHM2012公开数据集上的测试结果表明:所提方法可以在不需要末期退化信号的前提下预测轴承剩余寿命

预测结果明显高于现有报道的各类方法

均方根误差和平均绝对百分比误差分别为938.8 s与42.62%。在巴斯夫新材料有限公司某自动化测试系统数字孪生预测性维护平台的应用案例表明

所提方法具有实际工业场景下小样本旋转机械寿命预测的可行性。

Abstract

Aiming at accurately predicting the remaining useful life of the rotating machinery with a small size of samples

a digital twin driven method for health index construction and remaining useful life prediction for rotating machinery was proposed. The degradation behavior model was constructed by combining convolutional autoencoder and Weibull distribution theory. Firstly

the convolutional autoencoder was trained with the early degradation signal of the rotating machinery to learn the early degradation signal pattern. Then

the convolutional autoencoder was used to reconstruct the test signal and calculate the reconstruction error

which was mapped into the [0

1] interval as the health index. Finally

the Weibull reliability function was fitted according to the health index to predict the remaining useful life of the rotating machinery. The above steps were repeated during the continuous operation of the rotating machinery based on real-time data. The validation results of PHM2012 public data set show that the proposed method can predict the remaining useful life of bearings without the failure degradation signal

and the predicted results are significantly superior to those predicted by existing methods

with the root mean square error of 938.8 and the mean absolute percentage error of 42.62%. The application of this method in an automated test system digital twin predictive maintenance platform of BASF Advanced Chemicals Co.

Ltd. demonstrated the feasibility of the proposed method for few-shot life prediction of rotating machinery in practical industrial scenarios.

关键词

Keywords

references

CERRADA M, ZURITA G, CABRERA D, et al. Fault diagnosis in spur gears based on genetic algorithm and random forest [J]. Mechanical Systems and Signal Processing, 2016, 70-71: 87-103.

李乃鹏, 蔡潇, 雷亚国, 等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法 [J]. 机械工程学报, 2021, 57(20): 29-37, 46.

LI Naipeng, CAI Xiao, LEI Yaguo, et al. A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery [J]. Journal of Mechanical Engineering, 2021, 57(20): 29-37, 46.

李宏坤, 刘洪轶, 徐福健, 等. 连续小波最优重构尺度确定方法与故障早期识别 [J]. 机械工程学报, 2014, 50(17): 69-76.

LI Hongkun, LIU Hongyi, XU Fujian, et al. Method for the optimal continuous wavelet reconstruction scale determination and early fault classification [J]. Journal of Mechanical Engineering, 2014, 50(17): 69-76.

WANG Dong, ZHAO Yang, YI Cai, et al. Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings [J]. Mechanical Systems and Signal Processing, 2018, 101: 292-308.

冯辅周, 司爱威, 江鹏程. 小波相关排列熵和HMM在故障预测中的应用 [J]. 振动工程学报, 2013, 26(2): 269-276.

FENG Fuzhou, SI Aiwei, JIANG Pengcheng. Application of wavelet correlation permutation entropy and hidden Markov model to fault prognostic [J]. Journal of Vibration Engineering, 2013, 26(2): 269-276.

贾峰, 武兵, 熊晓燕, 等. 基于多维度排列熵与支持向量机的轴承早期故障诊断方法 [J]. 计算机集成制造系统, 2014, 20(9): 2275-2282.

JIA Feng, WU Bing, XIONG Xiaoyan, et al. Early fault diagnosis of bearing based on multi-dimension permutation entropy and SVM [J]. Computer Integrated Manufacturing Systems, 2014, 20(9): 2275-2282.

唐贵基, 王晓龙. 参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用 [J]. 西安交通大学学报, 2015, 49(5): 73-81.

TANG Guiji, WANG Xiaolong. Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing [J]. Journal of Xi'an Jiaotong University, 2015, 49(5): 73-81.

杨润贤, 郭林炀, 周正平, 等. 基于EVMD和SODN的滚动轴承故障识别研究 [J]. 机电工程, 2021, 38(10): 1221-1229.

YANG Runxian, GUO Linyang, ZHOU Zhengping, et al. Fault identification of rolling bearing based on EVMD SODN [J]. Journal of Mechanical Electrical Engineering, 2021, 38(10): 1221-1229.

宋亚, 夏唐斌, 郑宇, 等. 基于Autoencoder-BLSTM的涡扇发动机剩余寿命预测 [J]. 计算机集成制造系统, 2019, 25(7): 1611-1619.

SONG Ya, XIA Tangbin, ZHENG Yu, et al. Remaining useful life prediction of turbofan engine based on autoencoder-BLSTM [J]. Computer Integrated Manufacturing Systems, 2019, 25(7): 1611-1619.

KONG Zhengmin, CUI Yande, XIA Zhou, et al. Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics [J]. Applied Sciences, 2019, 9(19): 4156.

YU Shuyang, WU Zhenyu, ZHU Xinning, et al. A domain adaptive convolutional LSTM model for prognostic remaining useful life estimation under variant conditions [C]//2019 Prognostics and System Health Management Conference(PHM-Paris). Piscataway, NJ, USA: IEEE, 2019: 130-137.

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.

邓飞跃, 丁浩, 郝如江. 基于多尺度特征融合残差神经网络的旋转机械故障诊断 [J]. 振动与冲击, 2021, 40(24): 22-28, 35.

DENG Feiyue, DING Hao, HAO Rujiang. Fault diagnosis of rotating machinery based on residual neural network with multi-scale feature fusion [J]. Journal of Vibration and Shock, 2021, 40(24): 22-28, 35.

郑小霞, 陈广宁, 任浩翰, 等. 基于改进VMD和深度置信网络的风机易损部件故障预警 [J]. 振动与冲击, 2019, 38(8): 153-160, 179.

ZHENG Xiaoxia, CHEN Guangning, REN Haohan, et al. Fault detection of vulnerable units of wind turbine based on improved VMD and DBN [J]. Journal of Vibration and Shock, 2019, 38(8): 153-160, 179.

张弘斌, 袁奇, 赵柄锡, 等. 采用多通道样本和深度卷积神经网络的轴承故障诊断方法 [J]. 西安交通大学学报, 2020, 54(8): 58-66.

ZHANG Hongbin, YUAN Qi, ZHAO Bingxi, et al. Bearing fault diagnosis with multi-channel sample and deep convolutional neural network [J]. Journal of Xi'an Jiaotong University, 2020, 54(8): 58-66.

ZHAO Minghang, KANG M, TANG Baoping, et al. Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes [J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4290-4300.

陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用 [J]. 计算机集成制造系统, 2019, 25(1): 1-18.

TAOFei, LIU Weiran, ZHANG Meng, et al. Five-dimension digital twin model and its ten applications [J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.

陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用 [J]. 计算机集成制造系统, 2021, 27(1): 1-15.

TAO Fei, ZHANG He, QI Qinglin, et al. Theory of digital twin modeling and its application [J]. Computer Integrated Manufacturing Systems, 2021, 27(1): 1-15.

REN Zijie, WAN Jiafu, DENG Pan. Machine-learning-driven digital twin for lifecycle management of complex equipment [J]. IEEE Transactions on Emerging Topics in Computing, 2022, 10(1): 9-22.

许敏俊, 刘世民, 沈慧, 等. 数字孪生驱动下的弱刚性钻削毛刺控制 [J]. 计算机集成制造系统, 2023, 29(4): 1115-1126.

XU Minjun, LIU Shimin, SHEN Hui, et al. Burr control of weak rigid drilling process driven by digital twin [J]. Computer Integrated Manufacturing Systems, 2023, 29(4): 1115-1126.

付洋, 曹宏瑞, 郜伟强, 等. 数字孪生驱动的航空发动机涡轮盘剩余寿命预测 [J]. 机械工程学报, 2021, 57(22): 106-113.FU Yang, CAO Hongrui, GAO Weiqiang, et al. Digital twin driven remaining useful life prediction for aero-engine Turbine discs [J]. Journal of Mechanical Engineering, 2021, 57(22): 106-113.

WANG Jinjiang, YE Lunkuan, GAO R X, et al. Digital Twin for rotating machinery fault diagnosis in smart manufacturing [J]. International Journal of Production Research, 2019, 57(12): 3920-3934.

张西宁, 余迪, 刘书语. 基于迁移学习的小样本轴承故障诊断方法研究 [J]. 西安交通大学学报, 2021, 55(10): 30-37.

ZHANG Xining, YU Di, LIU Shuyu. Fault diagnosis method for small sample bearing based on transfer learning [J]. Journal of Xi'an Jiaotong University, 2021, 55(10): 30-37.

WEIBULL W. A statistical distribution function of wide applicability [J]. Journal of Applied Mechanics, 1951, 18(3): 293-297.

NECTOUX P, GOURIVEAU R, MEDJAHER K, et al. PRONOSTIA: an experimental platform for bearings accelerated degradation tests [C]//IEEE International Conference on Prognostics and Health Management(PHM2012). Piscataway, NJ, USA: IEEE, 2012: 1-8.

GUO Liang, LI Naipeng, JIA Feng, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings [J]. Neurocomputing, 2017, 240: 98-109.

HONG Sheng, ZHOU Zheng, ZIO E, et al. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method [J]. Digital Signal Processing, 2014, 27: 159-166.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

采用多尺度Mamba架构的装备剩余寿命预测方法

航空结构声发射数据质量评估与疲劳损伤监测方法

数字孪生驱动的机电设备远程运维方法

采用本征正交分解和长短期记忆网络模型的离心泵流场预测

双圈同轴式光纤位移传感器的输出特性