Deep Transformer Networks for Time Series Classification: The NPP Safety Case

• URL: http://arxiv.org/abs/2104.05448v1
• Date: Fri, 9 Apr 2021 14:26:25 GMT
• Title: Deep Transformer Networks for Time Series Classification: The NPP Safety Case
• Authors: Bing Zha, Alessandro Vanni, Yassin Hassan, Tunc Aldemir, Alper Yilmaz
• Abstract summary: An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time-dependent NPP simulation data. The Transformer can learn the characteristics of the sequential data and yield promising performance with approximately 99% classification accuracy on the testing dataset.
• Score: 59.20947681019466
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: A challenging part of dynamic probabilistic risk assessment for nuclear power plants is the need for large amounts of temporal simulations given various initiating events and branching conditions from which representative feature extraction becomes complicated for subsequent applications. Artificial Intelligence techniques have been shown to be powerful tools in time-dependent sequential data processing to automatically extract and yield complex features from large data. An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time-dependent NPP simulation data and to infer whether a given sequence of events leads to core damage or not. The training and testing datasets for the Transformer are obtained by running 10,000 RELAP5-3D NPP blackout simulations with the list of variables obtained from the RAVEN software. Each simulation is classified as "OK" or "CORE DAMAGE" based on the consequence. The results show that the Transformer can learn the characteristics of the sequential data and yield promising performance with approximately 99% classification accuracy on the testing dataset.

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