Rotor fault diagnosis of centrifugal pumps in nuclear power plants based on CWGAN-GP-CNN for imbalanced dataset

IF 3.3 3区工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Progress in Nuclear Energy Pub Date : 2024-10-16 DOI:10.1016/j.pnucene.2024.105500

Dai Cui , Runze Zhou , Honggang Li , Runan Hua , Zeyu Chen , Houlin Liu , Liang Dong , Zhiming Cheng , Xiaolin Wang

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引用次数: 0

Abstract

As a crucial device in nuclear power plants, centrifugal pumps undertake the critical role of cooling water circulation. Centrifugal pump rotor misalignment and unbalanced faults cause pump performance degradation, vibration increase, and equipment damage, thus seriously affecting the safety and reliability of nuclear power plants. In the process of centrifugal pump rotor fault, the difficulty in obtaining data samples and the limited amount of data can lead to an imbalance problem between the quantity of normal state and fault state samples in the dataset. In order to solve the problem, this paper proposed a CWGAN-GP model for generating rotor fault data based on CGAN and WGAN-GP models, and combined it with a two-stream CNN model to realize the rotor fault diagnosis with an imbalanced dataset. The quality and performance of the data generated by the proposed method were evaluated and validated in terms of visualization analysis, statistical indicators, and comparison with different data generation models. The results show that the CWGAN-GP model can generate high-quality data. Meanwhile, compared with other models on datasets with different degrees of imbalance, the two-stream CNN model is more effective in fault diagnosis on the expanded dataset by the CWGAN-GP model, and the improvement of fault diagnosis accuracy ranges from 1.40% to 13.33%.

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基于不平衡数据集的 CWGAN-GP-CNN 核电站离心泵转子故障诊断

作为核电站的重要设备，离心泵承担着冷却水循环的关键作用。离心泵转子不对中和不平衡故障会导致泵性能下降、振动加剧和设备损坏，从而严重影响核电站的安全性和可靠性。在离心泵转子故障处理过程中，由于数据样本获取困难，数据量有限，会导致数据集中正常状态样本与故障状态样本数量不平衡的问题。为了解决这一问题，本文在 CGAN 和 WGAN-GP 模型的基础上，提出了生成转子故障数据的 CWGAN-GP 模型，并将其与双流 CNN 模型相结合，实现了不平衡数据集下的转子故障诊断。从可视化分析、统计指标以及与不同数据生成模型的比较等方面对所提出方法生成的数据的质量和性能进行了评估和验证。结果表明，CWGAN-GP 模型可以生成高质量的数据。同时，在不同失衡程度的数据集上，与其他模型相比，CWGAN-GP 模型的双流 CNN 模型在扩展数据集上的故障诊断效果更好，故障诊断准确率提高了 1.40% 至 13.33%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Progress in Nuclear Energy 工程技术-核科学技术

CiteScore

5.30

自引率

14.80%

发文量

331

审稿时长

3.5 months

期刊介绍： Progress in Nuclear Energy is an international review journal covering all aspects of nuclear science and engineering. In keeping with the maturity of nuclear power, articles on safety, siting and environmental problems are encouraged, as are those associated with economics and fuel management. However, basic physics and engineering will remain an important aspect of the editorial policy. Articles published are either of a review nature or present new material in more depth. They are aimed at researchers and technically-oriented managers working in the nuclear energy field. Please note the following: 1) PNE seeks high quality research papers which are medium to long in length. Short research papers should be submitted to the journal Annals in Nuclear Energy. 2) PNE reserves the right to reject papers which are based solely on routine application of computer codes used to produce reactor designs or explain existing reactor phenomena. Such papers, although worthy, are best left as laboratory reports whereas Progress in Nuclear Energy seeks papers of originality, which are archival in nature, in the fields of mathematical and experimental nuclear technology, including fission, fusion (blanket physics, radiation damage), safety, materials aspects, economics, etc. 3) Review papers, which may occasionally be invited, are particularly sought by the journal in these fields.