Digital Twin Driven Few-Shot Prediction of Remaining Useful Life for Rotating Machinery

ZHANG Cheng; MA Ziwei; LIU Bin; SUN Zheng; XU Jun

doi:10.7652/xjtuxb202312017

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Digital Twin Driven Few-Shot Prediction of Remaining Useful Life for Rotating Machinery

更新时间：2025-07-09

- Digital Twin Driven Few-Shot Prediction of Remaining Useful Life for Rotating Machinery
- Vol. 57, Issue 12, Pages: 168-178(2023)
- 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：
- 基金信息：
- DOI：10.7652/xjtuxb202312017
  CLC： TH17;TP29
- Online First：10 December 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
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：

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.

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