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Prediction of bolt missing fault for multistage rotor by experimental test and analysis

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Abstract

The high-pressure rotor of aero-engine is assembled by numerous bolts under high manufacture precision. The connected structure is subjected to both axial force and transverse vibration during service, which may result in individual bolt loosen. In this study, the influence of bolt missing on the dynamic characteristics is analyzed by numerical simulation. A test rig capable of impact and frequency swee** experiment under axial tension was constructed. The vibration response features in the simulation were then extracted. The loss function of the mean absolute error and the decision method of extreme gradient boosting were used to predict the bolt missing position. The results show that the proposed model can reach a prediction precision of more than 90%. Moreover, the coefficient of determination evaluation index of the prediction effect reaches 0.9, which is significantly higher than those of other conventional models such as multivariate linear regression and multivariate adaptive regression spliness.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to their containing information that could compromise the privacy of research participant.

References

  1. Antoni N (2013) Contact separation and failure analysis of a rotating thermo-elastoplastic shrink-fit assembly[J]. Appl Math Model 37:2352–2363

    Article  MATH  Google Scholar 

  2. Fu C, Zhu WD, Zheng ZL, Sun CZ, Yang YF, Lu K* (2022) Nonlinear responses of a dual-rotor system with rub-impact fault subject to interval uncertain parameters. Mechanical Systems and Signal Processing 170:108827

  3. Whitney DE, Gilbert OL, Jastrzebski M (1994) Representation of geometric variations using matrix transforms for statistical tolerance analysis in assemblies. Res Eng Des 6(4):191–210

    Article  Google Scholar 

  4. **aokai Mu, Wang Y, Yuan Bo et al (2021) A New assembly precision prediction method of aeroengine high-pressure rotor system considering manufacturing error and deformation of parts[J]. J Manuf Syst 61:112–124

    Article  Google Scholar 

  5. Ding SY, Zheng XH (2021) Precision control of rotors assembly based on improved Jacobian-Torsor theory[J]. Acta Aeronautica et Astronautica Sinica 42:424670

    Google Scholar 

  6. Liu Y, Zhang M, Sun C et al (2019) A method to minimize stage-by-stage initial unbalance in the aero engine assembly of multistage rotors[J]. Aerosp Sci Technol 85:270–276

    Article  Google Scholar 

  7. Chuanzhi SUN, Zewei Liu, Yongmeng LIU, et al 2019 An Adjustment Method of Geometry and Mass Centers for Precision Rotors Assembly[J].IEEE ACCESS 2955124

  8. Youlin BAO, Lixin LI, Peng CAO et al (2021) Optimization of rotor assembly process of rotor initial unbalance of an aeroengine gas generator [J]. Trans Nan**g Univ Aeronaut Astronaut 38(1):132–139

    Google Scholar 

  9. Nassar SA, Veeram AB (2005) Ultrasonic control of fastener tightening using varying wave speed. J Pressure Vessel Technol 128:427–432

    Article  Google Scholar 

  10. Amerini F, Meo M (2011) Structural health monitoring of bolted joints using linear and nonlinear acoustic/ultrasound methods[J]. Struct Health Monit 10(6):659–672

    Article  Google Scholar 

  11. Eissa M, Saeed NA (2016) Nonlinear vibration control of a horizontally supported Jeffcott-rotor system[J]. J Vib Control 24(24):5898–5921

    Article  Google Scholar 

  12. Liu S, Ma Y, Zhang D et al (2012) Studies on dynamic characteristics of the joint in the aero-engine rotor system[J]. Eng Fail Anal 29(5):120–136

    Google Scholar 

  13. Sun W, Li T, Yang D et al (2020) Dynamic investigation of aeroengine high pressure rotor system considering assembly characteristics of bolted joints[J]. Eng Fail Anal 112:104510

    Article  Google Scholar 

  14. Hernández S, Menga E, Moledo S, Romera LE, Baldomir A, López C, Montoya MC (2017) Optimization approach for identification of dynamic parameters of localized joints of aircraft assembled structures, Aerosp. Sci Technol 69:538–549

    Google Scholar 

  15. Lia Y, Luoa Z, Liu J et al (2021) Dynamic modeling and stability analysis of a rotor-bearing system with bolted-disk joint. Mech Syst Signal Process 158:107778

    Article  Google Scholar 

  16. Beaudoin M-A, Behdinan K (2019) Analytical lump model for the nonlinear dynamic response of bolted flanges in aero-engine casings. Mech Syst Signal Process 115:14–28

    Article  Google Scholar 

  17. **chao Yu, Li L, Chen G, Yang M (2021) Dynamic modelling and vibration characteristics analysis for the bolted joint with spigot in the rotor system. Appl Math Model 94:306–331

    Article  Google Scholar 

  18. Breiman (1990) Classification and Regression tree Models. London: Chapman & Hall

  19. Chakraborty D, Elzarka H (2019) Early detection of faults in HVAC systems using an XGBoost model with a dynamic threshold. Energy Build 185:326–344

    Article  Google Scholar 

  20. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 785–794

  21. Trizoglou P, Liu X, Lin Zi (2021) Fault detection by an ensemble framework of Extreme Gradient Boosting (XGBoost) in the operation of offshore wind turbines. Renewable Energy 179:945–962

    Article  Google Scholar 

  22. Chen K, Chen H, Liu L et al (2019) Prediction of weld bead geometry of MAG welding based on XGBoost algorithm. Int J Adv Manuf Technol 101:2283–2295

    Article  Google Scholar 

  23. Zhang Z, Huang Y, Qin R et al (2021) XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling. J Manuf Proc 64:30–44

    Article  Google Scholar 

  24. Lin J, Qi C, Wan H et al (2021) Prediction of Cross-Tension Strength of Self-Piercing Riveted Joints Using Finite Element Simulation and XGBoost Algorithm. Chin J Mech Eng 34:36

    Article  Google Scholar 

  25. Phan QT, Wu YK, Phan QD (2021) A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs. Appl Energy 11:1100

    Google Scholar 

  26. Patnaik B, Mishra M, Bansal RC et al (2021) MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid. Appl Energy 285:116457

    Article  Google Scholar 

  27. Choi D-K (2019) Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels. Int J Precis Eng Manuf 20:129–138

    Article  Google Scholar 

  28. Yao X, Wang J (2017) Effects of load and structure parameters of aero-engine bolted joints on joint stiffness[J]. J Propuls Technol 38(2):424–433

    Google Scholar 

  29. Jofriet JC, Sze Y, Thompson JC (1981) The interface boundary conditions for bolted flanged connections[J]. J Pressure Vessel Technol 103(3):240–245

    Article  Google Scholar 

  30. Johnson KL (1985) Contact mechanics[J], J. Tribol 108(4):464

    Google Scholar 

  31. Shehadeh A, Alshboul O, Mamlook REA et al (2021) Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression[J]. Autom Constr 129:103827

    Article  Google Scholar 

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Funding

This research was sponsored by the National Natural Science Foundation of China (51905334), Shanghai Sailing Program (19YF1452400 and 19YF1418600).

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Authors and Affiliations

  1. Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China

    Cong Yue, Huashan Chi, Jianyu Fan, **angmin Zheng & Ziling Zhang

  2. AECC Shanghai Commercial Aircraft Engine Manufacturing CO., LTD, Shanghai, 201306, China

    Cong Yue

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  1. Cong Yue
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  2. Huashan Chi
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  3. Jianyu Fan
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  4. **angmin Zheng
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  5. Ziling Zhang
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Contributions

Conceptualization, C.Y. and HS.C.; methodology, C.Y.; validation, ZX.M. and JY.F..; formal analysis, ZL.Z.; investigation, C.Y.;. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Cong Yue.

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Yue, C., Chi, H., Fan, J. et al. Prediction of bolt missing fault for multistage rotor by experimental test and analysis. Int J Adv Manuf Technol 124, 4551–4562 (2023). https://doi.org/10.1007/s00170-022-10356-3

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  • DOI: https://doi.org/10.1007/s00170-022-10356-3

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