基于自校正异常诊断模型的传感器状态监测和信号重建研究

IF 3.3 3区 工程技术 Q1 NUCLEAR SCIENCE & TECHNOLOGY
Yudi Zhu , Xinzhi Zhou , Chengping Zhao , Junhui Yu , Jialiang Zhu , Tao Xu , Zhengxi He
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引用次数: 0

摘要

为确保安全高效的运行,状态监测在工业流程中至关重要。能够准确反映工业系统状态的传感器信号在这种监测中发挥着核心作用。然而,许多工业环境条件恶劣,尤其是核电站,增加了传感器发生故障的可能性。状态监测系统通过重构输入数据来检测异常情况,重构误差大则表明存在异常情况。多变量状态估计技术(MSET)是一种广泛应用于状态监测的非线性、非参数模型。传统的非线性模型假设训练数据和测试数据来自相同的分布。当模型遇到异常情况时,这一假设可能会导致重大误差,使检测和重建传感器状态的工作面临挑战。为了应对这些挑战,本文介绍了一种自校正异常诊断模型。与传统方法不同,该模型建立了一个专门的数据结构来存储正常的传感器模式,并生成一个动态的记忆矩阵,以适应工业流程的变化;所提出的方法将惩罚偏移投影与多尺度估计相结合,以减轻异常对估计结果的影响。此外,还开发了一种变量相关性分析方法,用于优化模型的输入特征选择。新方法可在转换后的信号空间中对异常数据进行自我修正,从而实现传感器状态的精确重建。利用核电站系统的真实传感器数据对模型的性能进行了验证。结果表明,即使在更恶劣的模拟条件下,所提出的模型也能显著增强信号重建和异常检测能力。与传统的非线性模型相比,新方法将减少异常干扰的指标提高了一个数量级。然而,我们并没有改变原有方法中高阶核的计算方法,它仍然面临着矩阵反演的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on sensor condition monitoring and signal reconstruction based on self-correcting anomaly diagnosis model
Condition monitoring is essential in industrial processes to ensure safe and efficient operations. Sensor signals, which accurately reflect the state of industrial systems, play a central role in this monitoring. However, the harsh conditions in many industrial environments, especially in nuclear power plants, increase the likelihood of sensor failures. Condition monitoring systems detect anomalies by reconstructing input data, with high reconstruction errors indicating the presence of anomalies. The Multivariate State Estimation Technique (MSET) is a widely used nonlinear, non-parametric model for condition monitoring. Traditional nonlinear models assume that training and test data come from the same distribution. This assumption can lead to significant errors when the model encounters anomalies, making it challenging to detect and reconstruct sensor states. To address these challenges, this paper introduces a self-correcting anomaly diagnosis model. Unlike traditional methods, this model establishes a dedicated data structure to store normal sensor patterns and generates a dynamic memory matrix that adapts to changes in industrial processes; The proposed method combines penalized offset projection with multi-scale estimation to mitigate the impact of anomalies on estimation results. Additionally, a variable correlation analysis method is developed to optimize input feature selection for the model. The new approach self-corrects anomalous data in a transformed signal space, achieving accurate reconstruction of sensor states. The model's performance is validated using real sensor data from a nuclear power plant system. Results demonstrate that the proposed model significantly enhances signal reconstruction and anomaly detection capabilities, even under more severe simulated conditions. Compared to traditional nonlinear models, the new method improves the metric for reducing anomaly interference by an order of magnitude. However, we did not change the calculation method of the higher-order kernel in the original method, which still faces the problem of matrix inversion.
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来源期刊
Progress in Nuclear Energy
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.
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