A Sensor Fault-Tolerant Accident Diagnosis System

Clicks: 227
ID: 267525
2020
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Abstract
Emergency situations in nuclear power plants are accompanied by an automatic reactor shutdown, which gives a big task burden to the plant operators under highly stressful conditions. Diagnosis of the occurred accident is an essential sequence for optimum mitigations; however, it is also a critical source of error because the results of accident identification determine the task flow connected to all subsequent tasks. To support accident identification in nuclear power plants, recurrent neural network (RNN)-based approaches have recently shown outstanding performances. Despite the achievements though, the robustness of RNN models is not promising because wrong inputs have been shown to degrade the performance of RNNs to a greater extent than other methods in some applications. In this research, an accident diagnosis system that is tolerant to sensor faults is developed based on an existing RNN model and tested with anticipated sensor errors. To find the optimum strategy to mitigate sensor error, Missforest, selected from among various imputation methods, and gated recurrent unit with decay (GRUD), developed for multivariate time series imputation based on the RNN model, are compared to examine the extent that they recover the diagnosis accuracies within a given threshold.
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choi2020sensorsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jeonghun Choi;Seung Jun Lee;Choi, Jeonghun;Lee, Seung Jun;
Journal sensors
Year 2020
DOI
10.3390/s20205839
URL
https://www.mdpi.com/1424-8220/20/20/5839##authors
Keywords
recurrent neural networks sensor fault mitigation sensor fault-tolerant accident diagnosis signal reconstruction

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