  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.
