IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v101y2017icp771-781.html
   My bibliography  Save this article

Crack characterisation using invariable feature extraction in stainless steel specimen used for absorber tubes of CSP applications via EMAT

Author

Listed:
  • Cheng, Liang
  • Kogia, Maria
  • Mohimi, Abbas
  • Kappatos, Vassilios
  • Selcuk, Cem
  • Gan, Tat-Hean

Abstract

Absorber tubes are one of the most critical components of parabolic trough Concentrated Solar Plants (CSPs). Due to the high temperatures where these tubes perform at with concentration of sunlight, it is very likely for them to get damaged such as crack and mass loss etc., and lose functionality of power generation. Therefore, the monitoring of their structural health via Non-Destructive Testing (NDT) techniques is regarded as essential for preventing them from being significantly defective and thereby reducing maintenance cost. Non-contact method is one of the best inspection candidates, which is more reliable to the tubes at high temperature through a review and the access to the absorber tubes is limited. In this paper, the crack detection and quantification for stainless steel specimen used for absorber tube using Electromagnetic Acoustic Transducers (EMATs) is presented. Through numerical and experimental studies, features are extracted to quantify the crack. Among these features, the ratio between the first edge echo and the second crack echo (Ac2/Ae1) is investigated as invariable feature to factors such as electromagnetic coupling, lift-off distance between EMATs and the specimen etc. In addition, the feature Ac2/Ae1 has linear relationship with the depth of the crack when the depth is more than 0.75 mm, which proves the feature Ac2/Ae1 is invariable for crack quantification via both numerical modelling and experimental studies.

Suggested Citation

  • Cheng, Liang & Kogia, Maria & Mohimi, Abbas & Kappatos, Vassilios & Selcuk, Cem & Gan, Tat-Hean, 2017. "Crack characterisation using invariable feature extraction in stainless steel specimen used for absorber tubes of CSP applications via EMAT," Renewable Energy, Elsevier, vol. 101(C), pages 771-781.
    • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:771-781
    DOI: 10.1016/j.renene.2016.09.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116308266
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.09.036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Papaelias, Mayorkinos & Cheng, Liang & Kogia, Maria & Mohimi, Abbas & Kappatos, Vassilios & Selcuk, Cem & Constantinou, Louis & Muñoz, Carlos Quiterio Gómez & Marquez, Fausto Pedro Garcia & Gan, Tat-H, 2016. "Inspection and Structural Health Monitoring techniques for Concentrated Solar Power plants," Renewable Energy, Elsevier, vol. 85(C), pages 1178-1191.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunxin Wu & Lei Han & Hai Gong & Jiangang Yang & Wei Li, 2017. "Effect of Coil Configuration on Conversion Efficiency of EMAT on 7050 Aluminum Alloy," Energies, MDPI, vol. 10(10), pages 1-11, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Natraj, & Reddy, K.S., 2023. "Investigations of thermo-structural instability on the performance of solar parabolic trough collectors," Renewable Energy, Elsevier, vol. 202(C), pages 381-393.
    2. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    3. Bijarniya, Jay Prakash & Sudhakar, K. & Baredar, Prashant, 2016. "Concentrated solar power technology in India: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 593-603.
    4. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    5. Villada, Carolina & Bonk, Alexander & Bauer, Thomas & Bolívar, Francisco, 2018. "High-temperature stability of nitrate/nitrite molten salt mixtures under different atmospheres," Applied Energy, Elsevier, vol. 226(C), pages 107-115.
    6. Lamnatou, Chr. & Chemisana, D., 2017. "Concentrating solar systems: Life Cycle Assessment (LCA) and environmental issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 916-932.
    7. Walczak, Magdalena & Pineda, Fabiola & Fernández, Ángel G. & Mata-Torres, Carlos & Escobar, Rodrigo A., 2018. "Materials corrosion for thermal energy storage systems in concentrated solar power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 22-44.
    8. Fuqiang, Wang & Ziming, Cheng & Jianyu, Tan & Yuan, Yuan & Yong, Shuai & Linhua, Liu, 2017. "Progress in concentrated solar power technology with parabolic trough collector system: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1314-1328.
    9. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    10. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:101:y:2017:i:c:p:771-781. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.