A data-driven adaptive fault diagnosis methodology for nuclear power systems based on NSGAII-CNN

With the development of digital information technology, nuclear energy systems are developing in the direction of intelligence and unmanned, which requires a higher demand for its safety, such as autonomous fault diagnosis. At present, the network structure model used in fault diagnosis usually needs professional design, which is time-consuming and labor-intensive, and the efficiency is low. To solve these problems, this paper proposes a data-driven adaptive fault diagnosis approach NSGAII-CNN. Firstly, the time-series data are mapped into two-dimensional images by Markov Transition Field, which preserves the time characteristics of the data and improves the fault diagnosis accuracy. Then, the NSGAII-CNN algorithm is proposed to realize the self-adaptive search of the network structure, which improves the construction speed of the fault diagnosis network structure model, thereby improving the diagnosis accuracy and efficiency. Finally, compared with the current three classical CNN architecture models designed by professionals, the methodology proposed in this paper has significant advantages in fault diagnosis and model structure construction. The proposed diagnosis method will provide operators with useful information and enhance the nuclear energy systems' self-diagnostic capabilities. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a data-driven adaptive fault diagnosis approach NSGAII-CNN, which maps time-series data into two-dimensional images and utilizes NSGAII-CNN algorithm to enhance the accuracy and efficiency of fault diagnosis, showing significant advantages compared to classical CNN architecture models.