DOE Fellow Roger Boza (left) with FIU DOD Fellows and Dr. Himanshu Upadhyay (middle) at ITAE
DOE Fellow Roger Boza attended International Test and Evaluation (ITAE) 2019 conference held in Orlando FL and presented a topic titled “Deep Learning with Big Data Analytics for Nuclear Decommissioning Application”. The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability, and reliability. Nuclear power plants are always expected to run every unit at maximum capacity, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear-decommissioning infrastructure provide many challenges with respect to maintenance or decommissioning of the buildings. A pilot-scale infrastructure was developed to implement structural health monitoring using scanning technologies, machine learning/deep learning and big data technologies. A Big Data Platform using the Hadoop Distributed File System (HDFS) was used in the study. Deep Convolutional Neural Network (CNN) was implemented in Python using the Keras library and TensorFlow architecture. The model was verified with 70/30 Cross Validation technique which achieved 97.1% accuracy during the training phase. The high accuracy of the CNN model demonstrates that with deep learning as a component of structural health monitoring can provide valuable information for the conditions of a nuclear facility.
Tags: Big Data, Deep Learning, ITAE