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A fault detector/classifier for closed-ring power generators using machine learning

Author

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  • Quintanilha, Igor M.
  • Elias, Vitor R.M.
  • da Silva, Felipe B.
  • Fonini, Pedro A.M.
  • da Silva, Eduardo A.B.
  • Netto, Sergio L.
  • Apolinário, José A.
  • de Campos, Marcello L.R.
  • Martins, Wallace A.
  • Wold, Lars E.
  • Andersen, Rune B.

Abstract

Condition-based monitoring of power-generation systems is naturally becoming a standard approach in industry due to its inherent capability of fast fault detection, thus improving system efficiency and reducing operational costs. Most such systems employ expertise-reliant rule-based methods. This work proposes a different framework, in which machine-learning algorithms are used for detecting and classifying several fault types in a power-generation system of dynamically positioned vessels. First, principal component analysis is used to extract relevant information from labeled data. A random-forest algorithm then learns hidden patterns from faulty behavior in order to infer fault detection from unlabeled data. Results on fault detection and classification for the proposed approach show significant improvement on accuracy and speed when compared to results from rule-based methods over a comprehensive database.

Suggested Citation

  • Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:reensy:v:212:y:2021:i:c:s0951832021001599
    DOI: 10.1016/j.ress.2021.107614
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    References listed on IDEAS

    as
    1. Shekhar, Chandra & Kumar, Amit & Varshney, Shreekant, 2020. "Load sharing redundant repairable systems with switching and reboot delay," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Zhou, Xiaoyi & Lu, Pan & Zheng, Zijian & Tolliver, Denver & Keramati, Amin, 2020. "Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    3. Nguyen, Khanh T.P. & Medjaher, Kamal, 2019. "A new dynamic predictive maintenance framework using deep learning for failure prognostics," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 251-262.
    4. Niu, Gang & Yang, Bo-Suk & Pecht, Michael, 2010. "Development of an optimized condition-based maintenance system by data fusion and reliability-centered maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 786-796.
    5. Jeong, Haedong & Park, Bumsoo & Park, Seungtae & Min, Hyungcheol & Lee, Seungchul, 2019. "Fault detection and identification method using observer-based residuals," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 27-40.
    6. Yu, Hongyang & Khan, Faisal & Garaniya, Vikram, 2015. "Risk-based fault detection using Self-Organizing Map," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 82-96.
    7. Kang, Hyun Gook & Jang, Seung-Cheol, 2006. "Application of condition-based HRA method for a manual actuation of the safety features in a nuclear power Plant," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 627-633.
    8. Bae, Suk Joo & Mun, Byeong Min & Chang, Woojin & Vidakovic, Brani, 2019. "Condition monitoring of a steam turbine generator using wavelet spectrum based control chart," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 13-20.
    9. Zio, Enrico & Gola, Giulio, 2009. "A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 78-88.
    10. Cipollini, Francesca & Oneto, Luca & Coraddu, Andrea & Murphy, Alan John & Anguita, Davide, 2018. "Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 12-23.
    11. Rocco S., Claudio M. & Zio, Enrico, 2007. "A support vector machine integrated system for the classification of operation anomalies in nuclear components and systems," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 593-600.
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    Cited by:

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    3. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Deep learning approach for energy efficiency prediction with signal monitoring reliability for a vinyl chloride monomer process," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Ma, Chenyang & Li, Yongbo & Wang, Xianzhi & Cai, Zhiqiang, 2023. "Early fault diagnosis of rotating machinery based on composite zoom permutation entropy," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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