arXiv subjects

William Richards

Publications and source records attributed to William Richards.

4 recordsLinked to original sources

arXiv2026

The PUR-1 Cyber-Physical Digital Twin

Digital twin technologies have the potential to improve operational flexibility and responsiveness capabilities of nuclear systems. To provide decision support, cyber event characterization, state estimation, predictive control, and real-time dynamic processing of operational data, however, an efficient digital twin needs to integrate multiple models (data-driven as well as physics-based) with explainability while at the same time maintain two-way synchronization with the physical facility at a time constant less than its operational cycle. In this work, we present the Purdue University Reactor One Digital Twin (PUR-1 DT), a cyber-physical digital twin with a complete high-fidelity physics-based and AI-driven virtual model stack (neutronics, thermal-hydraulics, point kinetics) which provides closed-loop explainable diagnostics, forecasting, predictive control, and action recommendation back to the reactor via two-way communications and a cyber-physical testbed. We demonstrate real-time synchronized state estimation and short-term forecasting over a full reactor operational cycle and conduct a series of benchmarking experiments to validate accuracy and latency. Our results show good agreement with experimental results and lay the groundwork for further development and experimental demonstration of DT-enabled functionalities in real-world facilities.

cs.CE↗

arXiv2025

Experimental Assessment of a Multi-Class AI/ML Architecture for Real-Time Characterization of Cyber Events in a Live Research Reactor

There is increased interest in applying Artificial Intelligence and Machine Learning (AI/ML) within the nuclear industry and nuclear engineering community. Effective implementation of AI/ML could offer benefits to the nuclear domain, including enhanced identification of anomalies, anticipation of system failures, and operational schedule optimization. However, limited work has been done to investigate the feasibility and applicability of AI/ML tools in a functioning nuclear reactor. Here, we go beyond the development of a single model and introduce a multi-layered AI/ML architecture that integrates both information technology and operational technology data streams to identify, characterize, and differentiate (i) among diverse cybersecurity events and (ii) between cyber events and other operational anomalies. Leveraging Purdue Universitys research reactor, PUR-1, we demonstrate this architecture through a representative use case that includes multiple concurrent false data injections and denial-of-service attacks of increasing complexity under realistic reactor conditions. The use case includes 14 system states (1 normal, 13 abnormal) and over 13.8 million multi-variate operational and information technology data points. The study demonstrated the capability of AI/ML to distinguish between normal, abnormal, and cybersecurity-related events, even under challenging conditions such as denial-of-service attacks. Combining operational and information technology data improved classification accuracy but posed challenges related to synchronization and collection during certain cyber events. While results indicate significant promise for AI/ML in nuclear cybersecurity, the findings also highlight the need for further refinement in handling complex event differentiation and multi-class architectures.

cs.LG↗

arXiv2025

A One-Class Explainable AI Framework for Identification of Non-Stationary Concurrent False Data Injections in Nuclear Reactor Signals

The transition of next generation advanced nuclear reactor systems from analog to fully digital instrumentation and control will necessitate robust mechanisms to safeguard against potential data integrity threats. One challenge is the real-time characterization of false data injections, which can mask sensor signals and potentially disrupt reactor control systems. While significant progress has been made in anomaly detection within reactor systems, potential false data injections have been shown to bypass conventional linear time-invariant state estimators and failure detectors based on statistical thresholds. The dynamic, nonlinear, multi-variate nature of sensor signals, combined with inherent noise and limited availability of real-world training data, makes the characterization of such threats and more importantly their differentiation from anticipated process anomalies particularly challenging. In this paper, we present an eXplainable AI (XAI) framework for identifying non-stationary concurrent replay attacks in nuclear reactor signals with minimal training data. The proposed framework leverages progress on recurrent neural networks and residual analysis coupled with a modified SHAP algorithm and rule-based correlations. The recurrent neural networks are trained only on normal operational data while for residual analysis we introduce an adaptive windowing technique to improve detection accuracy. We successfully benchmarked this framework on a real-world dataset from Purdue's nuclear reactor (PUR-1). We were able to detect false data injections with accuracy higher than 0.93 and less than 0.01 false positives, differentiate from expected process anomalies, and to identify the origin of the falsified signals.

eess.SY↗

arXiv2023

Online Tool Selection with Learned Grasp Prediction Models

Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. Tool-change, however, takes time. Choosing the order of grasps to perform, and corresponding tool-change actions, can improve system throughput; this is the topic of our work. The main challenge in planning tool change is uncertainty - we typically cannot see objects in the bin that are currently occluded. Inspired by queuing and admission control problems, we model the problem as a Markov Decision Process (MDP), where the goal is to maximize expected throughput, and we pursue an approximate solution based on model predictive control, where at each time step we plan based only on the currently visible objects. Special to our method is the idea of void zones, which are geometrical boundaries in which an unknown object will be present, and therefore cannot be accounted for during planning. Our planning problem can be solved using integer linear programming (ILP). However, we find that an approximate solution based on sparse tree search yields near optimal performance at a fraction of the time. Another question that we explore is how to measure the performance of tool-change planning: we find that throughput alone can fail to capture delicate and smooth behavior, and propose a principled alternative. Finally, we demonstrate our algorithms on both synthetic and real world bin picking tasks.

cs.RO↗