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A novel prognostic model of performance degradation trend for power machinery maintenance

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  • Zhou, Dengji
  • Zhang, Huisheng
  • Weng, Shilie

Abstract

Power machinery has two types of fault modes. The first type leads equipment to stop working, and the second one results in performance degradation. The second type should not be ignored, because of its safe, economic and environmental consequence. Aiming at the second type of fault modes, current prognostic model for the remaining useful life of equipment is usually based on the historical data of the equipment fault or malfunction, which can provide evidence for maintenance. However, this model just depends on the time based fault data, without taking the operation state into consideration. In this paper, a novel prognostic model of performance degradation trend is developed, which is based on current prognostic models for the remaining useful life. It combines the historical fault data and monitoring data in operation. This model can be used for maintenance optimization. Maintenance activities, according to the result of this model, actually combine the viewpoint of Time Based Maintenance and Condition Based Maintenance. Finally, compressor washing of a gas turbine engine is cited as an instance to validate this model. Maintenance strategies based on the new model infers that it not only keeps the reliability of equipment, but also reduces the maintenance cost.

Suggested Citation

  • Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    • Handle: RePEc:eee:energy:v:78:y:2014:i:c:p:740-746
    DOI: 10.1016/j.energy.2014.10.067
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    2. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    3. D. E. Ighravwe & S. A. Oke, 2017. "A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 961-973, November.
    4. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    5. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
    6. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    7. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    8. Yu, Weichao & Song, Shangfei & Li, Yichen & Min, Yuan & Huang, Weihe & Wen, Kai & Gong, Jing, 2018. "Gas supply reliability assessment of natural gas transmission pipeline systems," Energy, Elsevier, vol. 162(C), pages 853-870.
    9. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    10. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).

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