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Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model

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Abstract

Early fault warning for nuclear power machinery is conducive to timely troubleshooting and reductions in safety risks and unnecessary costs. This paper presents a novel intelligent fault prediction method, integrated probabilistic principal component analysis (PPCA), multi-resolution wavelet analysis, Bayesian inference, and RNN model for nuclear power machinery that consider data uncertainty and chaotic time series. After denoising the source data, the Bayesian PPCA method is employed for dimensional reduction to obtain a refined data group. A recurrent neural network (RNN) prediction model is constructed, and a Bayesian statistical inference approach is developed to quantitatively assess the prediction reliability of the model. By modeling and analyzing the data collected on the steam turbine and components of a nuclear power plant, the results of the goodness of fit, mean square error distribution, and Bayesian confidence indicate that the proposed RNN model can implement early warning in the fault creep period. The accuracy and reliability of the proposed model are quantitatively verified.

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References

  1. J.P. Ma, J. Jiang, Applications of fault detection and diagnosis methods in nuclear power plants: a review. Prog. Nucl. Energy 53, 255–266 (2011). https://doi.org/10.1016/j.pnucene.2010.12.001

    Article  Google Scholar 

  2. S. Mandal, B. Santhi, S. Sridhar et al., Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test. IEEE Trans. Nucl. Sci. 64, 1526–1534 (2017). https://doi.org/10.1109/TNS.2017.2697919

    Article  Google Scholar 

  3. Y.J. Gong, X.Y. Su, H. Qian et al., Research on fault diagnosis methods for the reactor coolant system of nuclear power plant based on D–S evidence theory. Ann. Nucl. Energy 112, 395–399 (2018). https://doi.org/10.1016/j.anucene.2017.10.026

    Article  Google Scholar 

  4. D. Wu, J. Wang, S.J. Du et al., Validation of the discharge force analysis method and key influence factors study. Nucl. Tech. 43(4), 040007 (2020). https://doi.org/10.11889/j.0253-3219.2020.hjs.43.040007. (in Chinese)

    Article  Google Scholar 

  5. W.F. Lyu, M.L. Chen, Q.Q. Huang et al., Development on calculation code CPAP for radioactive activation product of pressurized water reactor nuclear power plant. Nucl. Tech. 43(4), 040005 (2020). https://doi.org/10.11889/j.0253-3219.2020.hjs.43.040005. (in Chinese)

    Article  Google Scholar 

  6. J.T. Mo, A multi-field coupling simulation method for rod dropping behavior based on overset mesh. Nucl. Tech. 43(5), 050605 (2020). https://doi.org/10.11889/j.0253-3219.2020.hjs.43.050605. (in Chinese)

    Article  Google Scholar 

  7. H.Y. Xie, J.X. Li, Z.Y. Yan, et al., Research of critical reactivity control online early warning technology in nuclear power plant. In: Proceedings of the 2nd International Symposium on Software Reliability, Industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, Aug (2017), pp. 102–108. https://doi.org/10.1007/978-981-10-7416-5_13

  8. H. Qian, Si-Yun Lin, Miao Zheng, et al. Research on intelligent early-warning system of main pipeline in nuclear power plants based on hierarchical and multidimensional fault identification method. In: Proceedings of the International Conference on Intelligent Computing for Sustainable Energy and Environment, Sep (2017), pp. 195–205. https://doi.org/10.1007/978-981-10-6364-0_20

  9. J.H. Min, D.W. Kim, C.Y. Park, Demonstration of the validity of the early warning in online monitoring system for nuclear power plants. Nucl. Eng. Des. 349, 56–62 (2019). https://doi.org/10.1016/j.nucengdes.2019.04.028

    Article  Google Scholar 

  10. B.S. Peng, H. Xia, Y.K. Liu et al., Research on intelligent fault diagnosis method for nuclear power plant based on correlation analysis and deep belief network. Prog. Nucl. Energy 108, 419–427 (2018). https://doi.org/10.1016/j.pnucene.2018.06.003

    Article  Google Scholar 

  11. Y.T. Yao, J. Wang, M. Xie, L.Q. Hu et al., A new approach for fault diagnosis with full-scope simulator based on state information imaging in nuclear power plant. Ann. Nucl. Energy 141, 107274 (2020). https://doi.org/10.1016/j.anucene.2019.107274

    Article  Google Scholar 

  12. A.S. Qin, H. Qin, Y.R. Lv et al., Concurrent fault diagnosis based on Bayesian discriminating analysis and time series analysis with dimensionless parameters. IEEE Sens. J. 19, 2254–2265 (2019). https://doi.org/10.1109/JSEN.2018.2885377

    Article  Google Scholar 

  13. M. Mehrjoo, M.J. Jozani, M. Pawlak, Wind turbine power curve modeling for reliable power prediction using monotonic regression. Renew. Energy 147, 214–222 (2020). https://doi.org/10.1016/j.renene.2019.08.060

    Article  Google Scholar 

  14. S.A. Aye, P.S. Heyns, An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. Mech. Syst. Signal Process. 84, 485–498 (2017). https://doi.org/10.1016/j.ymssp.2016.07.039

    Article  Google Scholar 

  15. Y.C. Jiang, S. Yin, Recursive total principle component regression based fault detection and its application to vehicular cyber-physical systems. IEEE Trans. Ind. Inform. 14, 1415–1423 (2018). https://doi.org/10.1109/TII.2017.2752709

    Article  Google Scholar 

  16. T.Y. Gao, S. Yin, J.B. Qiu et al., A partial least squares aided intelligent model predictive control approach. IEEE Trans. Syst. Man. Cybern-Syst. 48, 2013–2021 (2018). https://doi.org/10.1109/TSMC.2017.2723017

    Article  Google Scholar 

  17. J. Herp, M.H. Ramezani, M. Bach-Andersen et al., Bayesian state prediction of wind turbine bearing failure. Renew. Energy 116, 164–172 (2018). https://doi.org/10.1016/j.renene.2017.02.069

    Article  Google Scholar 

  18. J.C. Li, Y.P. Cao, Y.L. Ying et al., A rolling element bearing fault diagnosis approach based on multifractal theory and gray relation theory. PLoS ONE 11, e0167587 (2016). https://doi.org/10.1371/journal.pone.0167587

    Article  Google Scholar 

  19. J. Liu, J.F. Liu, D.R. Yu et al., Fault detection for gas turbine hot components based on a convolutional neural network. Energies 11, 2149 (2018). https://doi.org/10.3390/en11082149

    Article  Google Scholar 

  20. Z.M. Liu, I.A. Karimi, Gas turbine performance prediction via machine learning. Energy 192, 116627 (2020). https://doi.org/10.1016/j.energy.2019.116627

    Article  Google Scholar 

  21. M. He, D. He, Deep Learning based approach for bearing fault diagnosis. IEEE Trans. Ind. Appl. 53, 3057–3065 (2017). https://doi.org/10.1109/TIA.2017.2661250

    Article  Google Scholar 

  22. H.S. Zhao, H.H. Liu, W.J. Hu et al., Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renew. Energy 127, 825–834 (2018). https://doi.org/10.1109/TIA.2017.2661250

    Article  Google Scholar 

  23. L. Wen, L. Gao, X.Y. Li, A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE Trans. Syst. Man Cybern-Syst. 49, 136–144 (2019). https://doi.org/10.1109/TSMC.2017.2754287

    Article  Google Scholar 

  24. H. Liu, J.Z. Zhou, Y. Zheng et al., Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Trans. 77, 167–178 (2018). https://doi.org/10.1016/j.isatra.2018.04.005

    Article  Google Scholar 

  25. H. Shahnazari, Fault diagnosis of nonlinear systems using recurrent neural networks. Chem. Eng. Res. Des. 153, 233–245 (2020). https://doi.org/10.1016/j.cherd.2019.09.026

    Article  Google Scholar 

  26. J.N. Wang, Y. Dou, Z.H. Wang et al., Multi-fault diagnosis method for wind power generation system based on recurrent neural network. Proc. Inst. Mech. Eng. A-J Pow. 233, 604–615 (2019). https://doi.org/10.1177/0957650919844065

    Article  Google Scholar 

  27. A.S. Palau, M.H. Dhada, K. Bakliwal et al., An industrial multi agent system for real-time distributed collaborative prognostics. Eng. Appl. Artif. Intell. 85, 590–606 (2019). https://doi.org/10.1016/j.engappai.2019.07.013

    Article  Google Scholar 

  28. J.J. Wang, P.L. Fu, L.B. Zhang et al., Multilevel information fusion for induction motor fault diagnosis. IEEE-ASME Trans. Mech. 24, 2139–2150 (2019). https://doi.org/10.1109/TMECH.2019.2928967

    Article  Google Scholar 

  29. W. Li, M.J. Peng, Y.K. Liu et al., Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method. Ann. Nucl. Energy 113, 105–117 (2018). https://doi.org/10.1016/j.anucene.2017.11.009

    Article  Google Scholar 

  30. M.R. Prusty, T. Jayanthi, J. Chakraborty et al., Feasibility of ANFIS towards multiclass event classification in PFBR considering dimensionality reduction using PCA. Ann. Nucl. Energy 99, 311–320 (2017). https://doi.org/10.1016/j.anucene.2016.09.015

    Article  Google Scholar 

  31. G.H. Wu, J.J. Tong, L.G. Zhang et al., Framework for fault diagnosis with multi-source sensor nodes in nuclear power plants based on a Bayesian network. Ann. Nucl. Energy 122, 297–308 (2018). https://doi.org/10.1016/j.anucene.2018.08.050

    Article  Google Scholar 

  32. R. Sharifi, R. Langari, Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models. Mech. Syst. Signal Process. 85, 638–650 (2017). https://doi.org/10.1016/j.ymssp.2016.08.028

    Article  Google Scholar 

  33. J.W. Xiang, Y.T. Zhong, H.F. Gao, Rolling element bearing fault detection using PPCA and spectral kurtosis. Measurement 75, 180–191 (2015). https://doi.org/10.1016/j.measurement.2015.07.045

    Article  Google Scholar 

  34. M.E. Tipping, C.M. Bishop, Mixtures of probabilistic principal component analyzers. Neural Comput. 11, 443–482 (1999). https://doi.org/10.1162/089976699300016728

    Article  Google Scholar 

  35. J.T. Liu, C.H. Wu, J.W. Wang, Gated recurrent units based neural network for time heterogeneous feedback recommendation. Inform. Sci. 423, 50–65 (2018). https://doi.org/10.1016/j.ins.2017.09.048

    Article  Google Scholar 

  36. D. You, X. Shen, Y. Zhu et al., A quantitative validation method of kriging metamodel for injection mechanism based on Bayesian statistical inference. Metals 9, 493 (2019). https://doi.org/10.3390/met9050493

    Article  Google Scholar 

  37. B. Cai, X. Kong, Y. Liu et al., Application of Bayesian networks in reliability evaluation. IEEE Trans. Ind. Inform. 15, 2146–2157 (2019). https://doi.org/10.1109/TII.2018.2858281

    Article  Google Scholar 

  38. W.J. Cui, B. Cao, Y.X. Chen, Uncertainty analysis of Gaussian plume model based on Bayesian MCMC method. Nucl. Tech. 43(4), 040009 (2020). https://doi.org/10.11889/j.0253-3219.2020.hjs.43.040009. (in Chinese)

    Article  Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen, 518172, China

    Jun Ling & Gao-Jun Liu

  2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, China

    Jia-Liang Li, Xiao-Cheng Shen & Dong-Dong You

  3. Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China

    Jun Ling

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  1. Jun Ling
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  2. Gao-Jun Liu
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  3. Jia-Liang Li
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  4. Xiao-Cheng Shen
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  5. Dong-Dong You
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Corresponding authors

Correspondence to Jia-Liang Li or Dong-Dong You.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 51875209), the Guangdong Basic and Applied Basic Research Foundation (No. 2019B1515120060), and the Open Funds of State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment.

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Ling, J., Liu, GJ., Li, JL. et al. Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model. NUCL SCI TECH 31, 75 (2020). https://doi.org/10.1007/s41365-020-00792-9

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  • DOI: https://doi.org/10.1007/s41365-020-00792-9

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