卷积-门控自注意力多源数据融合的泵组智能异常检测方法  

Intelligent Anomaly Detection Method of Pump Set Based on Convolve-gated Self-attention Multi-source Data Fusion

作　　者：孙原理 宋志浩 Sun Yuanli;Song Zhihao(Naval Research Academy,Beijing,100161,China)

机构地区：[1]海军研究院,北京100161

出　　处：《核动力工程》2025年第2期300-305,共6页Nuclear Power Engineering

摘　　要：针对核动力泵组在运行过程中多变工况下难以利用多源检测信号进行诊断的问题,本文提出一种利用深度学习网络融合多源数据的泵组智能异常检测方法。该方法利用卷积神经网络(CNN)对多源数据进行融合,能够有效地对多源数据之间的关系进行分析。采用自注意力机制提取具有注意力权值的输入数据融合特征,使所构建的智能异常检测模型具有自主适应不同类型输入数据的能力,保证了所提方法在多源数据场景下的核动力泵组智能异常状态检测的准确度,同时加入残差块提升模型训练效果。通过搭建泵组故障模拟试验台来验证该方法的可靠性和准确性,结果表明,本文所提检测方法能够有效融合多源数据之间的信息特征,在此基础上能够充分完成泵组在运行过程中多变工况下故障诊断的任务,且具有较高的诊断精度。To address the challenge of diagnosing nuclear power pump set under varying operating conditions using multi-source detection signals,this paper proposes an intelligent anomaly detection method based on deep learning for pump set by fusing multi-source data.The method employs Convolutional Neural Networks(CNN)to fuse multi-source data,effectively analyzing the relationships among diverse data sources.A self-attention mechanism is adopted to extract fusion features of input data with attention weights,enabling the constructed intelligent anomaly detection model to autonomously adapt to different types of input data.This ensures the high accuracy of the proposed method in detecting abnormal states of nuclear power pump set under multi-source data scenarios.Additionally,residual blocks are incorporated to enhance model training performance.The reliability and accuracy of the method are validated through a pump set fault simulation test bench.The results demonstrate that the proposed detection method effectively integrates the informational features of multi-source data,enabling the reliable diagnosis of faults in pump set under variable operating conditions with high diagnostic precision.

关 键 词：泵组 故障诊断 特征融合 深度学习 多源数据 

分 类 号：TL334[核科学技术—核技术及应用]