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PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals

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  • Na, Kyumin
  • Yoon, Heonjun
  • Kim, Jaedong
  • Kim, Sungjong
  • Youn, Byeng D.

Abstract

This paper proposes a novel method for boiler tube leak localization in a thermal power plant, using acoustic emission sensors. In industrial settings, due to computational and storage capacity, the measured acoustic emission signal is often processed through the use of descriptors, such as the root mean square (RMS), which is related to the signal energy. Computational and storage capacity issues make it difficult to use conventional methods, including time difference of arrival, which uses a high-sampling-rate signal. In addition, the measured RMS may have uncertainty that arises due to sensor disturbance or unpredictable process conditions. Thus, this study newly proposes an approach called probabilistic energy-ratio-based localization (PERL) to estimate the location of a boiler tube leak. In the proposed approach, acoustic dissipation theory is used to calculate the ratio of the signal energy from the specific band energy. To account for background noises and sensor disturbance, the uncertainty of the measured RMS is characterized in a probabilistic manner. Using this information, the probability that a boiler tube leak has occurred at a specific location is estimated hypothetically. Case studies confirm that the proposed method enables localization of a boiler tube leak position with high accuracy.

Suggested Citation

  • Na, Kyumin & Yoon, Heonjun & Kim, Jaedong & Kim, Sungjong & Youn, Byeng D., 2023. "PERL: Probabilistic energy-ratio-based localization for boiler tube leaks using descriptors of acoustic emission signals," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005385
    DOI: 10.1016/j.ress.2022.108923
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    References listed on IDEAS

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    1. Cholette, Michael E. & Yu, Hongyang & Borghesani, Pietro & Ma, Lin & Kent, Geoff, 2019. "Degradation modeling and condition-based maintenance of boiler heat exchangers using gamma processes," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 184-196.
    2. Taleb-Berrouane, Mohammed & Khan, Faisal & Hawboldt, Kelly, 2021. "Corrosion risk assessment using adaptive bow-tie (ABT) analysis," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Liu, Cuiwei & Wang, Yazhen & Li, Xinhong & Li, Yuxing & Khan, Faisal & Cai, Baoping, 2021. "Quantitative assessment of leakage orifices within gas pipelines using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Eleftheroglou, Nick & Zarouchas, Dimitrios & Loutas, Theodoros & Alderliesten, Rene & Benedictus, Rinze, 2018. "Structural health monitoring data fusion for in-situ life prognosis of composite structures," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 40-54.
    5. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Fang, Xiaolei & Cai, Xiao & Yan, Tao, 2021. "Remaining useful life prediction based on a multi-sensor data fusion model," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Kim, Kyeongsu & Lee, Gunhak & Park, Keonhee & Park, Seongho & Lee, Won Bo, 2021. "Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Fan, Xudong & Wang, Xiaowei & Zhang, Xijin & ASCE Xiong (Bill) Yu, P.E.F., 2022. "Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    8. Li, Xin & Zhong, Xiang & Shao, Haidong & Han, Te & Shen, Changqing, 2021. "Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. B. Castanier & C. Bérenguer & A. Grall, 2003. "A sequential condition‐based repair/replacement policy with non‐periodic inspections for a system subject to continuous wear," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(4), pages 327-347, October.
    10. Kim, Yochan & Park, Jinkyun & Presley, Mary, 2021. "Selecting significant contextual factors and estimating their effects on operator reliability in computer-based control rooms," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    11. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    12. Manjurul Islam, M.M. & Kim, Jong-Myon, 2019. "Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 55-66.
    13. Oh, ChoHwan & Lee, Jeong Ik, 2020. "Real time nuclear power plant operating state cognitive algorithm development using dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    14. Zhang, Qiongfang & Xu, Nan & Ersoy, Daniel & Liu, Yongming, 2022. "Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    15. Heidary, Roohollah & Groth, Katrina M., 2021. "A hybrid population-based degradation model for pipeline pitting corrosion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    Full references (including those not matched with items on IDEAS)

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