A federated deep domain adaptation-based framework for nuclear power steam turbines considering privacy-preserving

IF 3.1 Q2 ENGINEERING, INDUSTRIAL
Bingtao Hu, Ruirui Zhong, Junjie Song, Jingren Guo, Yong Wang, Shanhe Lou, Jianrong Tan
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引用次数: 0

Abstract

With the increasing awareness of environmental protection, sustainable manufacturing has become an important component in various industries. As an essential foundation for sustainable strategy, safe and reliable operation and maintenance of nuclear power resources is crucial, requesting agile and precise response and diagnosis of equipment failure signals. Due to security requirements, nuclear power plants strictly isolate operating data and form an actual data island. Simultaneously, the insufficient amount of fault sample data makes it difficult to establish an accurate fault diagnosis model. How to establish a stable and reliable nuclear power steam turbine vibration fault diagnosis model across different nuclear power plants and nuclear equipment has become a big problem. To achieve secure model aggregation without violating client privacy, federated learning (FL) has become a research hot spot for model aggregation, but it ignores the differences between source clients and fails to capture domain-invariant features during local training, which hinders its further development. To address this challenge, a federated deep domain adaptation-based framework considering privacy-preserving (FL-DDA) is proposed for operations and maintenance in nuclear power plants. The framework performs feature extraction locally in source nuclear power plants and targets nuclear power plants, such that the features are shared securely without revealing data privacy. At the same time, domain adversarial training is integrated into the local model training to realise the transfer of vibration fault diagnosis knowledge. Furthermore, an adaptive weight mechanism is devised to facilitate the adaptive adjustment of model weights in federated aggregation. Finally, a desensitised vibration dataset in nuclear power steam turbines is applied for validation, and FL-DDA is compared with other existing methods. Under the premise of data privacy security, the proposed FL-DDA framework proves to outperform its peers in vibration fault diagnosis and domain adaptation.

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考虑隐私保护的联合深度域自适应核电汽轮机框架
随着环保意识的增强,可持续制造已成为各行业的重要组成部分。作为可持续发展战略的重要基础,核电资源的安全可靠运行与维护至关重要,要求对设备故障信号进行敏捷、准确的响应与诊断。出于安全要求,核电站严格隔离运行数据,形成实际的数据孤岛。同时,由于故障样本数据量不足,难以建立准确的故障诊断模型。如何建立一个稳定、可靠的跨核电站、跨核设备的核电汽轮机振动故障诊断模型已成为一个大问题。为了在不侵犯客户端隐私的前提下实现安全的模型聚合,联邦学习(FL)成为模型聚合的研究热点,但它忽略了源客户端之间的差异,在局部训练过程中未能捕捉到域不变特征,阻碍了其进一步发展。为了解决这一问题,提出了一种考虑隐私保护的基于联邦深度域自适应框架(FL-DDA)。该框架在源核电站和目标核电站进行局部特征提取,从而在不泄露数据隐私的情况下安全地共享特征。同时,将域对抗训练与局部模型训练相结合,实现振动故障诊断知识的传递。在此基础上,设计了自适应权值机制,实现了联邦聚合中模型权值的自适应调整。最后,利用核电汽轮机脱敏振动数据集进行验证,并与现有方法进行了比较。在保证数据隐私安全的前提下,所提出的FL-DDA框架在振动故障诊断和域自适应方面优于同类框架。
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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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