An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Power transient identification

Various microreactor designs under development aim at fulfilling electricity and heat production requirements affordably and reliably in a variety of applications, like power generation for remote communities and military bases. The fission battery deployment strategy for microreactors provides battery-like simplicity for its instal-lation and operation. A successful fission battery must be cost competitive, have unit standardization, enable flexible installation, possess high reliability, and feature unattended operations. Due to the design and opera-tional features of microreactors, successful unattended operations of the system must be enabled through an on-line monitoring (OLM) system that can effectively and reliably detect and diagnose any fault in the reactor. This research introduces the concept and approach of an intelligent Fault Detection and Diagnosis Monitoring System (FDDMS) that can provide power transient dependent fault detection and diagnosis to fit within a future autonomous control framework. Specifically, this paper develops the first FDDMS module, the Power Transient Module, to reliably identify reactor operations as steady state, ramping up in power, or ramping down in power using data from a broad-scope simulator. Knowledge of the current power transient in the reactor is paramount for effective fault detection and diagnosis in any operational regime. This paper explores various data-driven methods to accurately evaluate the power transients: 1) Principal Component Analysis and Support Vector Machines, 2) Deep Neural Networks, and 3) Convolutional Neural Networks. To prepare the sensor data collected from the simulator for training the data-driven models, several preprocessing techniques were tested to optimize for evaluation accuracy. The final model selected for the Power Transient Module produced perfect evaluation results for several power transients, including in previously unseen regimes. Foundation for further module development of the FDDMS was established. Future works will see the implementation of the remaining FDDMS architecture.