控制棒驱动机构故障隔离与诊断的优化器比较
Comparison of Optimizers for Fault Isolation and Diagnostics of Control Rod Drives
AI总结：
针对小型模块化反应堆控制棒驱动系统的故障检测与诊断问题，研究对比了多种优化器对一维卷积神经网络相关模型的训练效果，发现Nadam优化器性能更优，同时指出应将运行次数作为优化器比较的超参数，为相关系统故障诊断优化提供参考。
摘要：
本文探究了GE-Hitachi公司BWRX-300小型模块化反应堆（SMR）的控制棒驱动系统（CRDS）中故障检测与诊断（FDD）的优化问题，研究重点为电动精细运动控制棒驱动（FMCRD）伺服电机。本研究利用多台FMCRD协同运动完成控制棒调节的特性，提出了一种深度学习方法：采用基于一维卷积神经网络（1D CNN）的自编码器进行异常检测，使用编码器-解码器结构的1D CNN分类器完成故障分类。研究通过Simulink模型模拟正常与故障运行状态，监测电流和电磁转矩参数，并对故障隔离与故障分类模型的训练过程进行了优化。研究评估了多种优化器的性能，包括自适应矩估计（Adam）、Nesterov Adam（Nadam）、随机梯度下降（SGD）以及均方根传播（RMSProp），结果表明Nadam凭借其自适应梯度和Nesterov分量，在隔离和分类任务中表现出相对更优的性能。本研究强调了在经验性优化器比较中，将运行次数（每次运行采用不同的模型初始参数集）作为超参数考量的重要性，为提升SMR控制系统的FDD性能以及1D CNN在FDD领域的应用提供了关键参考见解。
英文摘要：
This paper explores the optimization of fault detection and diagnostics (FDD) in the Control Rod Drive System (CRDS) of GE-Hitachi's BWRX-300 small modular reactor (SMR), focusing on the electrically powered fine motion control rod drive (FMCRD) servomotors. Leveraging the coordinated motion of multiple FMCRDs for control rod adjustments, the study proposes a deep learning approach, utilizing one-dimensional convolutional neural network (1D CNN)-based autoencoders for anomaly detection and encoder-decoder structured 1D CNN classifiers for fault classification. Simulink models simulate normal and fault operations, monitoring electric current and electromagnetic torque. The training of the fault isolation and fault classification models is optimized. Various optimizers, including Adaptive Moment Estimation (Adam), Nesterov Adam (Nadam), Stochastic Gradient Descent (SGD), and Root Mean Square Propagation (RMSProp), are evaluated, with Nadam demonstrating a relatively superior performance across the isolation and classification tasks due to its adaptive gradient and Nesterov components. The research underscores the importance of considering the number of runs (each run has a different set of initial model parameters) as a hyperparameter during empirical optimizer comparisons and contributes insights crucial for enhancing FDD in SMR control systems and for the application of 1D CNN to FDD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
