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The connection between digital-twin model and physical space for rotating blade: an atomic norm-based BTT undersampled signal reconstruction method

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Abstract

Digital twin that shows great potential in different fields may serve as the enabling technology for the health monitoring of aero-engine blade. However, due to the harsh conditions inside the aero-engine, one of the most challenging issues for the implementation of digital-twin-based blade health monitoring is the lack of an accurate connection method between the digital-twin model and the physical entity for rotating blade. Wherein, the key is how to measure the blade data accurately. The emerging blade tip timing (BTT), an effective non-contact measurement method for blades, has received extensive attention recently. Whereas, due to the limited probes that are allowed to be installed on the engine casing, the BTT signal is generally incomplete and under-sampling, which makes it very difficult to reconstruct the blade vibration parameters from the measured data. In this study, a novel paradigm for blade vibration parameter reconstruction with super-resolution from the undersampled BTT signal is proposed based on atomic norm soft thresholding (AST), which may offer accurate blade vibration information for the construction and updating of blade digital-twin model. Unlike the conventional reconstruction method that generally needs the interested signal to be sparse under a finite discrete dictionary for successful reconstruction, the proposed AST-based blade vibration parameter reconstruction method can take any continuous value in the frequency domain from the measurement data with fewer sampling numbers and higher under-sampling rate. Both numerical simulation and experimental verification are utilized to verify the validity of the proposed method. The comparative results indicate that the proposed method performs well in resisting “incomplete.” Meanwhile, the proposed method performs better than state-of-the-art methods under conditions with fewer data.

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Acknowledgements

We thank the support given by the National Natural Science Foundation of China (52105117 & 51875433), the National Key Research and Development Program of China (2020YFB2010800), and the Funds for Distinguished Young talent of Shaanxi Province (No. 2019JC-04).

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Authors and Affiliations

  1. The State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Ruochen Jin, Laihao Yang, Zhibo Yang, Yu Sun, Ruqiang Yan & Xuefeng Chen

  2. The Mechanical and Materials Engineering Department, Worcester Polytechnic Institute, Worcester, MA, 01609-2280, USA

    Zhu Mao

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  1. Ruochen Jin
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  2. Laihao Yang
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  3. Zhibo Yang
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  4. Yu Sun
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  5. Zhu Mao
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  6. Ruqiang Yan
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  7. Xuefeng Chen
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Contributions

Ruo Chen Jin: orignal draft, method implemention, experimental validation Laihao Yang: supervisor, funding, method Zhibo Yang (phdapple@mail.xjtu.edu.cn): co-supervisor, funding, method Yu Sun: method, discussion Ruqiang Yan: funding, discussion Xuefeng Chen: discussion.

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Correspondence to Laihao Yang.

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The authors declare that they have no conflict of interest.

Replication of results

Experimental data and the Python based source code for replication of the case studies will be available from the corresponding author upon request.

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Responsible Editor: Chao Hu

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Topical Collection: Advanced Optimization Enabling Digital Twin Technology.

Guest Editors: C. Hu, V. A. Gonzalez, T. Kim, O. San, Z. Hu, P. Zheng

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Jin, R., Yang, L., Yang, Z. et al. The connection between digital-twin model and physical space for rotating blade: an atomic norm-based BTT undersampled signal reconstruction method. Struct Multidisc Optim 66, 27 (2023). https://doi.org/10.1007/s00158-022-03436-1

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