基于改进红鸢优化算法与LSTM的核电换热器寿命预测方法

doi:10.11996/JG.j.2095-302X.2025051085

图学学报 ›› 2025, Vol. 46 ›› Issue (5): 1085-1093.DOI: 10.11996/JG.j.2095-302X.2025051085

基于改进红鸢优化算法与LSTM的核电换热器寿命预测方法

鲜思渔¹^,²(), 赵泽田³, 吴轩宇¹(), 冯毅雄¹^,²^,³, 薛杨⁴, 张志峰³

¹ 贵州大学机械工程学院，贵州贵阳 550025
² 贵州大学省部共建公共大数据国家重点实验室，贵州贵阳 550025
³ 浙江大学流体动力基础件与机电系统全国重点实验室，浙江杭州 310027
⁴ 中广核工程有限公司，广东深圳 518116

收稿日期:2025-05-07 接受日期:2025-07-10 出版日期:2025-10-30 发布日期:2025-09-10
通讯作者:吴轩宇(1998-)，男，特聘教授，博士。主要研究方向为产品数字化设计与智能制造等。E-mail：xuanyuwu@zju.edu.cn
第一作者:鲜思渔(2000-)，男，硕士研究生。主要研究方向为核电设备预测性维修等。E-mail：2985712029@qq.com
基金资助:
浙江省重点研发计划(2024C01029);浙江省重点研发计划(2025C01023);贵州大学开放基金(PBD2024-0515)

Life prediction method of nuclear power heat exchanger based on improved red kite optimization algorithm and LSTM

XIAN Siyu¹^,²(), ZHAO Zetian³, WU Xuanyu¹(), FENG Yixiong¹^,²^,³, XUE Yang⁴, ZHANG Zhifeng³

¹ School of Mechanical Engineering, Guizhou University, Guiyang Guizhou 550025, China
² State Key Laboratory of Public Big Data, Co-built by the Province and Ministry, Guizhou University, Guiyang Guizhou 550025, China
³ National Key Laboratory of Fluid Power Fundamental Components and Mechatronic Systems, Zhejiang University, Hangzhou Zhejiang 310027, China
⁴ China General Nuclear Power Engineering Co., Ltd., Shenzhen Guangdong 518116, China

Received:2025-05-07 Accepted:2025-07-10 Published:2025-10-30 Online:2025-09-10
First author：XIAN Siyu (2000-), master student. His main research interests cover predictive maintenance of nuclear power equipment, etc. E-mail：2985712029@qq.com
Supported by:
Zhejiang Provincial Key Research and Development Program(2024C01029);Zhejiang Provincial Key Research and Development Program(2025C01023);Guizhou University Open Fund(PBD2024-0515)

摘要/Abstract

摘要：

随着核电设备数量的增加和种类的多样化，基于状态检测的预测性维修策略逐渐成为核电厂关注的重点，特别是对关键设备如冷却水换热器进行剩余使用寿命预测。因此提出了一种结合改进红鸢优化算法(IROA)与长短期记忆网络(LSTM)的换热器寿命预测方法，旨在克服传统方法在超参数调优方面的局限性，并提高预测精度。针对现有方法中初始种群多样性不足导致过早收敛的问题，引入遗传算法中的交叉和变异操作改进了ROA算法，以增强种群多样性和跳出局部最优解的能力。通过利用历史退化数据训练LSTM模型，详细分析了不同超参数组合对模型性能的影响，并证明了优化后的超参数组合能够显著提升预测效果。为了验证该方法的有效性，将IROA-LSTM与其他常见预测方法(如SVM、CNN、RNN、标准LSTM)及其他几种优化算法进行了对比实验，并且还设置了噪声干扰实验。结果表明，IROA-LSTM不仅在各项性能指标上表现出色，还展示了较强的鲁棒性和稳定性，能够在不同条件下保持较高的预测精度。为制定科学合理的维修策略提供了可靠的数据支持，有助于提高核电厂设备运行的安全性和经济性。

关键词: 寿命预测, 预测性维修, 换热器, 改进红鸢优化, 长短期记忆网络

Abstract:

With the increase in the quantity and diversification of nuclear power equipment, predictive maintenance strategies based on condition detection have gradually become a focus of attention for nuclear power plants, especially for predicting the remaining service life of key equipment, such as cooling water heat exchangers. A heat exchanger life prediction method combining an improved red kite optimization algorithm (IROA) with a long short-term memory network (LSTM) was proposed to address the limitations of traditional methods in hyperparameter optimization and improve prediction accuracy. In response to the problem of premature convergence caused by insufficient initial population diversity in existing methods, crossover and mutation operations from genetic algorithms were introduced to improve the ROA algorithm, in order to enhance population diversity and the ability to escape from local optima. With an LSTM model trained using historical degradation data, we conducted a detailed analysis of the impact of different hyperparameter combinations on model performance, demonstrating that the optimized hyperparameter combinations could significantly improve prediction performance. To verify the effectiveness of the proposed method, we conducted comparative experiments between IROA-LSTM and other common prediction methods such as SVM, CNN, RNN, and standard LSTM, as well as comparative experiments with several other optimization algorithms, along with noise interference tests. The results indicated that IROA-LSTM not only performed well in various performance indicators, but also demonstrated strong robustness and stability, maintaining high prediction accuracy under different conditions. This provided reliable data support for developing scientific and reasonable maintenance strategies, thereby contributing to improved safety and economic efficiency of nuclear power plant equipment operation.

Key words: life prediction, predictive maintenance, heat exchanger, improve red kite optimization, long short-term memory

中图分类号:

TH183
TH39

鲜思渔, 赵泽田, 吴轩宇, 冯毅雄, 薛杨, 张志峰. 基于改进红鸢优化算法与LSTM的核电换热器寿命预测方法[J]. 图学学报, 2025, 46(5): 1085-1093.

XIAN Siyu, ZHAO Zetian, WU Xuanyu, FENG Yixiong, XUE Yang, ZHANG Zhifeng. Life prediction method of nuclear power heat exchanger based on improved red kite optimization algorithm and LSTM[J]. Journal of Graphics, 2025, 46(5): 1085-1093.

图/表 9

图1 红鸢优化算法流程

Fig. 1 Red kite optimization algorithm flow

图2 LSTM网络架构图

Fig. 2 LSTM network architecture diagram

图3 IROA结合LSTM的方法流程图

Fig. 3 Flowchart of the IROA combined with LSTM methodology

表1 超参数组合

Table 1 Hyperparameter combination

超参数类型	Num_layers	Batch size	Lstm_size
取用值	[1、2、 3、4]	[32、64、 128、256]	[32、64、 128、256]

图4 不同超参数下测试效果图((a) 层数为1时的实验效果图；(b) 层数为2时的实验效果图；(c) 层数为3时的实验效果图；(d) 层数为4时的实验效果图)

Fig. 4 Test effect diagram under different hyperparameters ((a) Experimental results of the model with one layer; (b) Experimental results of the model with two layer; (c) Experimental results of the model with three layer;(d) Experimental results of the model with four layer)

图5 IROA-LSTM与不同方法的比较验证图

Fig. 5 Comparison and validation of IROA-LSTM with different methods

图6 IROA与不同优化方法的比较效果图

Fig. 6 Comparative effectiveness diagram of IROA with various optimization methods

图7 高斯白噪声干扰下的实验效果图((a) 5%高斯白噪声的噪声干扰效果图；(b) 10%高斯白噪声的噪声干扰效果图)

Fig. 7 The experimental results under Gaussian white noise interference ((a) Effect of 5% Gaussian white noise interference; (b) Effect of 10% Gaussian white noise interference)

图8 各个传感器的SHAP值分析

Fig. 8 SHAP value analysis for each sensor

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基于改进红鸢优化算法与LSTM的核电换热器寿命预测方法

Life prediction method of nuclear power heat exchanger based on improved red kite optimization algorithm and LSTM

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