Reliability Dynamic Prediction Method for Remanufactured Products Based on Data-model Integration and Transfer

doi:10.3969/j.issn.1004-132X.2026.04.025

Abstract

Abstract:

To address the problems that the reliability data samples of remanufactured products were scarce， leading to difficulties in accurately predicting their reliability status during service， a dynamic reliability prediction method for remanufactured products was proposed， integrating substance-field degradation data from similar products with model transfer fine-tuning. Firstly， the “physical form” and “field properties” degradation indicators affecting product reliability were analyzed using the substance-field model. Then， a comprehensive degradation index reflecting the multi-dimensional substance-field degradation characteristics of products was constructed using a linear regression model， and a three-stage similarity calculation method was designed to screen and transfer historical degradation data from similar products for sample expansion. Secondly， to address the spatial coupling and temporal dependency characteristics of the historical substance-field degradation data of similar products， a reliability prediction model was established based on a convolutional long short-term memory neural network. Furthermore， the parameters of the prediction model were dynamically adjusted through deep transfer learning techniques to improve the prediction accuracy for the reliability of remanufactured products under personalized service scenarios. Finally， the proposed prediction method was validated using a remanufactured spindle system as a case study， and the coefficient of determination （R²） of the prediction results reache 0.92， which indicates the effectiveness of the method.

Key words: remanufactured product, reliability prediction, performance degradation, similarity calculation, deep transfer learning

摘要：

针对再制造产品可靠性因数据样本少而导致其服役过程中可靠性状态难以准确预测的问题，提出了融合相似产品物-场退化数据与模型迁移微调的再制造产品可靠性动态预测方法。首先，利用物-场模型分析影响产品可靠性的“物性”与“场性”退化指标，利用线性回归模型构建反映产品多维物-场退化特征的综合退化指标，并设计一个三阶段相似性计算方法来筛选和迁移相似产品历史退化数据以扩充样本。然后，针对相似产品历史物-场退化数据的空间耦合性和时间依赖性特征，建立基于卷积长短期神经网络的可靠性预测模型。进一步，通过深度迁移技术动态调整预测模型参数，提高模型对再制造产品在个性化服役场景下的可靠性预测精度。最后，以某再制造主轴系统为例对所提预测方法进行验证，预测结果的决定系数（R²）达到0.92，表明了该方法的有效性。

关键词: 再制造产品, 可靠性预测, 性能退化, 相似性计算, 深度迁移学习

CLC Number:

TH122

FENG Yukang, ZHU Shuo, JIANG Zhigang, YAN Wei, ZHANG Hua. Reliability Dynamic Prediction Method for Remanufactured Products Based on Data-model Integration and Transfer[J]. China Mechanical Engineering, 2026, 37(4): 1007-1015.

冯雨康, 朱硕, 江志刚, 鄢威, 张华. 数据与模型融合迁移的再制造产品可靠性动态预测方法[J]. 中国机械工程, 2026, 37(4): 1007-1015.

Figures/Tables 10

Fig.1 Process for predicting the reliability of remanufactured products during service period

Fig.2 Analysis of product degradation indicators via functional decomposition and substance-field model

Fig.3 Illustration of time window processing

Fig.4 Framework of fine-tuning method for reliability prediction model

Tab.1 Data types of substance-field degradation of the spindle system

项目	退化指标名称	符号
物	主轴形变量/μm	S₁
	主轴裂纹深度/μm	S₂
	轴承磨损深度/μm	S₃
	轴承径向间隙量/μm	S₄
	油膜厚度/μm	S₅
	油箱密封泄漏量/（mL·min $- 1$ ）	S₆
场	主轴径向跳动量/μm	F₁
	功率损耗率/%	F₂
	轴承温升比率/%	F₃
	噪声/dB	F₄
	振动/（mm·s $- 1$ ）	F₅

Tab.1 Data types of substance-field degradation of the spindle system

项目	退化指标名称	符号
物	主轴形变量/μm	S₁
	主轴裂纹深度/μm	S₂
	轴承磨损深度/μm	S₃
	轴承径向间隙量/μm	S₄
	油膜厚度/μm	S₅
	油箱密封泄漏量/（mL·min $- 1$ ）	S₆
场	主轴径向跳动量/μm	F₁
	功率损耗率/%	F₂
	轴承温升比率/%	F₃
	噪声/dB	F₄
	振动/（mm·s $- 1$ ）	F₅

Tab.2 Details of training set and test set data

	训练集	测试集1	测试集2
样本数量	3	1	1
运行周期	从运行到失效	0~150	150~200
服役生命周期	1	—	—

Fig.5 Visualization of substance-field degradation data

Tab.3 Network structure and training parameter settings of CNN-LSTM

网络设置	设置结果
卷积层数	1
池化层数	1
LSTM层数	2
全连接层数	2
卷积核数量	16
卷积核尺寸	2×2
池化窗口大小	1×1
LSTM-1神经元个数	32
LSTM-2神经元个数	16
全连接层-1神经元个数	8
全连接层-2神经元个数	4
时间窗大小	5
迭代次数	200
批量大小	8
学习率	0.01

Fig.6 Reliability curve prediction results before and after fine-tuning of test set 2

Tab.4 Performance comparison of different prediction models

模型	σ_RMSE	R²
CNN	0.010	0.68
CNN_TL	0.007	0.83
LSTM	0.009	0.76
LSTM_TL	0.006	0.86
CNN-LSTM_TL	0.004	0.92

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