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.

翻译：本文研究了GE-Hitachi BWRX-300小型模块化反应堆控制棒驱动系统中故障检测与诊断的优化问题，重点关注电动精细运动控制棒驱动伺服电机。研究利用多个精细运动控制棒驱动机构的协调运动进行控制棒调节，提出了一种深度学习方法：采用基于一维卷积神经网络的自编码器进行异常检测，并利用编码器-解码器结构的一维卷积神经网络分类器进行故障分类。通过Simulink模型模拟正常运行与故障运行状态，监测电流与电磁转矩。研究优化了故障隔离模型与故障分类模型的训练过程，评估了多种优化器（包括自适应矩估计、Nesterov Adam、随机梯度下降和均方根传播）的性能。结果表明，由于具备自适应梯度与Nesterov动量特性，Nesterov Adam在隔离与分类任务中均表现出相对更优的性能。本研究强调了在实证优化器比较中将训练轮次（每轮次具有不同的初始模型参数集）作为超参数的重要性，为增强小型模块化反应堆控制系统的故障检测与诊断能力，以及一维卷积神经网络在故障检测与诊断中的应用提供了关键见解。