Advertisement

Input Data Dimensionality Reduction of Abnormality Diagnosis Model for Nuclear Power Plants

  • Jae Min Kim
  • Gyumin Lee
  • Suckwon Hong
  • Seung Jun Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)

Abstract

Nuclear power plants are diagnosed by operators according to the alarms and plant parameters that can be identified in the main control room. The operators are trained to conduct tasks in any cases by following an operating procedure. When a component has in malfunction, the operator must choose the appropriate abnormal operating procedures to stabilize the plant. However, the operators take a high burden because this task requires complex judgement with large amounts of information in a short time. To support the operators, this paper studied the data preprocessing methods to develop the nuclear power plant abnormal state diagnosis system using deep learning algorithms. A nuclear power plant simulator was used to produce training data which includes more than 2800 variables recorded in the given time. It is necessary to reduce the dimensionality of the generated data to achieve the best estimation of the training. There are two ways to reduce the dimensionality of the data: feature selection and feature extraction methods. Abnormal operating procedures of the advanced pressurized water reactor 1400 were analyzed to select parameters related with abnormal events. On the other hand, principal components analysis was used as one of the feature extraction methods. Preprocessed data through two methods were trained by the same deep learning algorithm, gated recurrent unit. The data selected by humans and the data extracted by considering the relationship among the variables showed different performance for diagnosing the plant state. The results showed that it is advantageous for the developing diagnosis model to learn and judge through the feature extraction method.

Keywords

Principal components analysis Deep learning Gated recurrent unit Nuclear power plant 

Notes

Acknowledgements

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20171510102040).

References

  1. 1.
    Miller, D.W., et al.: Experience with the hierarchical method for diagnosis of faults in nuclear power plant systems. Reliab. Eng. Syst. Saf. 44(3), 297–311 (1994)CrossRefGoogle Scholar
  2. 2.
    Horiguchi, M., Fukawa, N., Nishimura, K.: Development of nuclear power plant diagnosis technique using neural networks. In: Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, Seattle, WA, USA, pp. 279–282 (1991)Google Scholar
  3. 3.
    Lee, S.J., Seong, P.H.: A dynamic neural network based accident diagnosis advisory system for nuclear power plants. Prog. Nucl. Energy 46(3–4), 268–281 (2005)CrossRefGoogle Scholar
  4. 4.
    Zimek, A., Schubert, E., Kriegel, H.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. 5, 363–387 (2012)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, pp. 1–59 (2002)Google Scholar
  6. 6.
    Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv preprint arXiv:1412.3555 (2014)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jae Min Kim
    • 1
  • Gyumin Lee
    • 1
  • Suckwon Hong
    • 1
  • Seung Jun Lee
    • 1
  1. 1.Ulsan National Institute of Science and TechnologyUlsanSouth Korea

Personalised recommendations