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Research on fault diagnosis technology of simulated altitude test facility based on multi-optimization strategy, real-time data transfer, and the M-H attention-RF algorithm

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Abstract

The simulated altitude test facility, as an important means to verify the performance, characteristics, and evaluation result criteria of aero-engines, has a pivotal engineering significance in the process of aero-engine development and promotion of application. In order to cope with the drawbacks of traditional techniques for experimental processes, this paper proposes the real-time data extraction and transfer techniques with multiple optimization strategies and the fault diagnosis technology of simulated altitude test facility with an improved optimization algorithm is propose, Firstly, the optimization strategy based on peak shaving + peak fast processing and token bucket instructions with multi-threaded parallel processing flow allocation call logic is used to realize the test data for fast extraction and migration demand, and then the overall data transfer function is optimized in granularity improvement schemes by using the abstraction optimization strategy mechanism based on Direct Routing mode to maximise real-time targets while ensuring correspondence and completeness of test data. Finally, the random forest algorithm with Multi-Head Attention optimization is used to implement the diagnostic technology research of the simulated altitude test facility under two scenarios under the data-driven mode, and the analytical comparison and validation results with the unimproved and optimized Random Forest algorithm are given. The results indicate that the amount of test data synchronization reaches 300 + lines per second, the accuracy of fault diagnosis identification is increased by 30% at the highest degree, and the proposed improvement research has a very high degree of application value and innovativeness.

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Data availability

The data that support the findings of this study are available from [China Gas Turbine Establishment] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [China Gas Turbine Establishment].

References

  1. Hou M, Liu D (2012) Development and prospect of aero-engine altitude table[J]. Aviat Sci Technol 03:1–4

    Google Scholar 

  2. Cao J (2018) Current status, challenges and prospects of aero-engine simulation technology research[J]. Propuls Technol 39(05):961–970

    Google Scholar 

  3. Afkhami S, Fouladi N, Fard MP (2023) Experimental and numerical investigation of transient starting of pre-evacuated exhaust diffuser in high altitude ground test[J]. Aerosp Sci Technol 133:108111

    Article  Google Scholar 

  4. Zhou Q, qing Guo Y, Zhao W et al (2023) Research on altitude table data visualization and data flow migration technology based on multi-framework integration. PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-2555451/v1

  5. Peres RS, Jia X, Lee J, Sun K, Colombo AW, Barata J (2020) Industrial artificial intelligence in industry 4.0 - systematic review, challenges and outlook. IEEE Access 8:220121–220139. https://doi.org/10.1109/ACCESS.2020.3042874

    Article  Google Scholar 

  6. Liu J, Xi W, Liu X et al (2022) Precise control of constant pressure chamber pressure based on control distribution[J]. Propulsion Technology 10:383–391

    Google Scholar 

  7. Patents; Patent Application Titled (2017) Real-time synchronization of data between disparate cloud data sources. Published Online (USPTO 20170116206)[J]. Computer Weekly News

  8. Bansal N, Soni K, Sachdeva S (2022) Journey of Database Migration from RDBMS to NoSQL Data Stores. In: Sachdeva S, Watanobe Y, Bhalla S (eds) Big-Data-Analytics in Astronomy, Science, and Engineering. BDA 2021. Lecture Notes in Computer Science, vol 13167. Springer, Cham. https://doi.org/10.1007/978-3-030-96600-3_12

    Chapter  Google Scholar 

  9. Lehman TJ, Cozzia A, Xiong Y (2020) Hitting the distributed computing Sweet Spot with TSpaees[J]. Comput Netw 35(4):24–30

    Google Scholar 

  10. Addad RA, Dutra DLC, Bagaa M, Taleb T, Flinck H (2020) Fast service migration in 5G trends and scenarios. IEEE Network 34(2):92–98. https://doi.org/10.1109/MNET.001.1800289

    Article  Google Scholar 

  11. Prasath N, Sreemathy J (2021) A new approach for cloud data migration technique using talend ETL tool. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp 1674–1678, https://doi.org/10.1109/ICACCS51430.2021.9441898

  12. Klingerman S (2020) Oracle cloud’s new free tier and always free oracle autonomous database[J]. Database Trends Appl 34(2)

  13. Ouafiq EM, Saadane R, Chehri A, Wahbi M (2022) Data lake conception for smart farming: A data migration strategy for big data analytics. In: Zimmermann A, Howlett RJ, Jain LC (eds) Human Centred Intelligent Systems Smart Innovation, Systems and Technologies, vol 310. Springer, Singapore. https://doi.org/10.1007/978-981-19-3455-1_15

  14. Zhang J (2021) Research on software architecture technology of aero-engine health monitoring unit[J]. Autom Appl (05):74–77. https://doi.org/10.19769/j.zdhy.2021.05.020

  15. Chengnan Wu, Tian Q, Chen Si (2021) Research on real-time synchronization technology of incremental data based on power regulation and control characteristics and business requirements[J]. Power Energy 42(05):527–530

    Google Scholar 

  16. Wang J, Lin Z (2021) Research on real-time synchronization technology of maritime formation mission planning data based on ECA rules[J]. Ship Electron Eng 41(08):25–29

    CAS  Google Scholar 

  17. Guangniu Su (2022) Huawei Cloud GaussDB deepens database root technology to help enterprises’ digital transformation[J]. China SME 06:67–68

    Google Scholar 

  18. Wang Y, Wang X (2023) CNN-based active suspension sensor fault diagnosis [J/OL]. Control Eng:1–6. https://doi.org/10.14107/j.cnki.kzgc.20220513

  19. Zhang S, Zhang S, Wang B, Habetler TG (2020) Deep learning algorithms for bearing fault diagnostics—A comprehensive review. IEEE Access 8:29857–29881. https://doi.org/10.1109/ACCESS.2020.2972859

    Article  Google Scholar 

  20. Schmid M, Gebauer E, Hanzl C, Endisch C (2021) Active model-based fault diagnosis in reconfigurable battery systems. IEEE Trans Power Electron 36(3):2584–2597. https://doi.org/10.1109/TPEL.2020.3012964

    Article  ADS  Google Scholar 

  21. Tamilselvan Prasanna, Wang Pingfeng (2013) Failure diagnosis using deep belief learning based health state classification. Reliab Eng Syst Saf 115:124–135. https://doi.org/10.1016/j.ress.2013.02.022

    Article  Google Scholar 

  22. Li Z, Wang W, Wang P (2022) Improved MSEA-CNN for ship motor bearing fault diagnosis[J]. Ship Sci Technol 44(14):119–122

    Google Scholar 

  23. Chen T, Chen D, Lv R (2021) Research on bearing fault diagnosis method with integrated learning algorithm[J]. Sci Technol Bull 37(04):57–61. https://doi.org/10.13774/j.cnki.kjtb.2021.04.011

    Article  Google Scholar 

  24. Huang D, Li S, Qin N, Zhang Y (2021) Fault diagnosis of high-speed train bogie based on the Improved-CEEMDAN and 1-D CNN algorithms. IEEE Trans Instrum Meas 70:3508811. https://doi.org/10.1109/TIM.2020.3047922

    Article  Google Scholar 

  25. Guo J, Dan Z, Li L (2010) All-digital altitude simulation test stand intake and exhaust pressure automatic control system[C]. Intelligent Networks and Intelligent Systems, International Workshop on

  26. Pei X, Zhu M, Zhang S (2016) An empirical formula iterative method for the calculation of flow characteristics of special valves[J]. Gas Turbine Testing and Research 29(5):5

    Google Scholar 

  27. Wang Yubo, Quan Zhenhua, Zhao Yaohua, Wang Lincheng, Jing Heran (2022) Operation mode performance and optimization of a novel coupled air and ground source heat pump system with energy storage: Case study of a hotel building[J]. Renew Energy 201(P1):889–903. https://doi.org/10.1016/J.RENENE.2022.11.016

    Article  Google Scholar 

  28. Hossam M, Castillo GE, Cardenas BJL (2022) Maximizing the electricity cost-savings for local distribution system using a new peak-shaving approach based on mixed integer linear programming[J]. Electronics 11(21):3610. https://doi.org/10.3390/ELECTRONICS11213610

    Article  Google Scholar 

  29. Ma Xuhan, Longsheng Wu, Zhao Kunpeng, Chen Qingyu (2016) A multi-token bucket-based data storm suppression unit[J]. Microelectronics Comput 33(09):84–88. https://doi.org/10.19304/j.cnki.issn1000-7180.2016.09.019

    Article  Google Scholar 

  30. Gao R, Ye Q, Liu W, Han N, Yang G (2022) A cloud database query optimization method based on multi-threaded communication mechanism[J/OL]. Radio Eng:1-10

  31. Chen L, Tang Y, Qi H (2022) Design and implementation of multi-threaded reproducible DGEMV for Fetion processors[J]. Comput Sci 49(10):27–35

    CAS  Google Scholar 

  32. Chen Yidan, Zhao Min, Guo Zheng (2022) A fast response and scheduling method for power supply resources based on cloud computing task allocation[J]. Autom Technol Appl 41(10):60–63. https://doi.org/10.20033/j.1003-7241.(2022)10-0060-05

    Article  CAS  Google Scholar 

  33. Aali Pant, Ramana GV (2022) Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model[J]. Geotext Geomembr 50(6):1188–1198. https://doi.org/10.1016/J.GEOTEXMEM.2022.08.003

    Article  Google Scholar 

  34. Kourosh A, Shirin M, Subodh Chandra P, Asish S, Indrajit C, Trong NT, Scott J, Marta S, Jaroslaw S, Van Nam T (2023) Improving species distribution models for dominant trees in climate data-poor forests using high-resolution remote sensing[J]. Ecol Model 475. https://doi.org/10.1016/J.ECOLMODEL.2022.110190

  35. Sun Hao (2022) Nearest neighbor retrieval for massive high-dimensional data based on improved random forest[J]. Automat Technol Appl 41(11):73–76. https://doi.org/10.20033/j.1003-7241.(2022)11-0073-04

    Article  Google Scholar 

  36. Liu Yupeng, Wei Hongrui (2022) Convolutional long-short term memory network with multi-head attention mechanism for traffic flow prediction[J]. Sensors 22(20):7994. https://doi.org/10.3390/S22207994

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  37. Zheng Yangfeng, Shao Zheng, Gao Zhanghao, Deng Mingming, Zhai Xuesong (2022) Optimizing the online learners’ verbal intention classification efficiency based on the multi-head attention mechanism algorithm[J]. Int J Found Comput Sci 33:717–733. https://doi.org/10.1142/S0129054122420114

    Article  Google Scholar 

  38. Zhang Xiaodong, Qin Zixuan, Li Min (2023) Residual life prediction of aero engines based on multi-feature fusion[J/OL]. Comput Syst Appl 32(03):95–103. https://doi.org/10.15888/j.cnki.csa.008958

    Article  Google Scholar 

  39. Zhang Qi, Shuangyuan Yu, Yin H (2023) A neural collaborative filtering social recommendation algorithm based on graph attention [J/OL]. Comput Sci 50(02):115–122

    Google Scholar 

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Authors and Affiliations

  1. Northwestern Polytechnical University, Xian, 710000, People’s Republic of China

    Qifan Zhou, Yingqing Guo, Wanli Zhao, Kejie Xu, Kun Wang & Zhenglong Wu

  2. Xi’an Institute of Modern Control Technology, Xian, 710000, People’s Republic of China

    Hao Sun

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  1. Qifan Zhou
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Correspondence to Qifan Zhou.

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Zhou, Q., Guo, Y., Zhao, W. et al. Research on fault diagnosis technology of simulated altitude test facility based on multi-optimization strategy, real-time data transfer, and the M-H attention-RF algorithm. Multimed Tools Appl 83, 28729–28760 (2024). https://doi.org/10.1007/s11042-023-16738-3

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