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Virtual–real fusion maintainability verification based on adaptive weighting and truncated spot method

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Maintainability is an important general quality characteristic of products. Insufficient maintainability will lead to long maintenance time and high maintenance cost, thus affecting the availability of products. Maintainability verification is an important means to ensure maintainability meets design requirements. However, the cost of traditional real maintainability verification method is very high, and the virtual maintenance method has insufficient verification accuracy due to the lack of large maintenance force feedback when the human body is moving. In order to reduce the evaluation error and test sample size, the paper conducts maintainability verification based on the mixed physical and virtual maintainability test scenarios. Aiming at the problem that traditional methods are difficult to deal with the real test information and synchronous virtual simulation information in the test process, this study proposes a virtual–real fusion maintainability evaluation algorithm based on adaptive weighting and truncated SPOT (Sequential Posterior Odd Test) method. It can weigh real test information and virtual human simulation information adaptively to obtain a virtual–real fusion maintainability test sample. Then, the SPOT method is used to evaluate the maintainability of small samples. The adjustment of valve clearance, replacement of air filter element and replacement of starting motor maintenance tasks of ship engine are taken as examples for demonstration. The virtual–real fusion and virtual maintainability verification methods are respectively used for verification, and compared with the physical maintenance scenario constructed by 3D printing, indicating that the accuracy of virtual–real fusion maintainability test verification is 89%, while the virtual maintainability verification is only 33%.
Rocznik
Strony
738--746
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, School of Intelligent Science and Technology, De Ya Road, 109, Changsha, Hunan 410073, P. R. China
autor
  • China Airborne Missile Academy, Luoyang, Henan 471009, P. R.China
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, School of Intelligent Science and Technology, De Ya Road, 109, Changsha, Hunan 410073, P. R. China
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, School of Intelligent Science and Technology, De Ya Road, 109, Changsha, Hunan 410073, P. R. China
autor
  • National University of Defense Technology, Laboratory of Science and Technology on Integrated Logistics Support, School of Intelligent Science and Technology, De Ya Road, 109, Changsha, Hunan 410073, P. R. China
Bibliografia
  • 1. Bernard F, Zare M, Sagot J C, Paquin R. Using Digital and Physical Simulation to Focus on Human Factors and Ergonomics in Aviation Maintainability, Human Factors, 2019, 62(1): 37-54, https://doi.org/10.1177/0018720819861496.
  • 2. Brown J, Kelly. Maintainability Verification for Cost-Effective Execution, Annual Reliability and Maintainability Symposium (RAMS), 2018:1-4, https://doi.org/10.1109/RAM.2018.8463020.
  • 3. Chen Z Y, Zhang X W, Ye L Y. Multi-sensor data weighted fusion method based on LMS algorithm. Computer engineering and application, 2014, 50(20): 86-90, https://doi.org/10.3778/j.issn.1002-8331.1401-0273.
  • 4. Dong B C, Song B W, Liang Q W, Mao Z Y. Research on Small Sample Maintainability Experimentation and Evaluation of Weapon System, Acta Armamentar II, 2011, 32(3): 327-330.
  • 5. Ge X Y, Zhou Q X, Liu Z Q. Assessment of Space Station On-Orbit Maintenance Task Complexity. Reliability Engineering & System Safety, 2019, 106661, https://doi.org/10.1016/j.ress.2019.106661.
  • 6. Ge Z X, Zhang Y, Yang Y M, Luo X. A New Maintainability Evaluation Method Based on Virtual–real Fusion Scene Construction, Scientific Programming, 2022: 6547225, https://doi.org/10.1155/2022/6547225.
  • 7. Ge Z X, Qi Z Q, Luo X, Yang Y M, Zhang Y. Multistage Bayesian fusion evaluation technique endorsing immersive virtual maintenance, Measurement, 2021,177: 109344, https://doi.org/10.1016/j.measurement.2021.109344.
  • 8. Goulden E C. An analytic approach to performing a maintainability demonstration. IEEE Transactions on Reliability, 1990, 39(1), 19-22, 25, https://doi.org/10.1109/24.52628.
  • 9. Grochow K, Martin S L, Hertzmann A. Style-based inverse kinematics. ACM Trans on Graphics, 2004, 23(3):522-531, https://doi.org/10.1145/1015706.1015755.
  • 10. Guo Z Y, Zhou D, Chen J Y, Geng J. Using virtual reality to support the product’s maintainability design: Immersive maintainability verification and evaluation system, Computers in Industry, 2018, 101:41-50, https://doi.org/10.1016/j.compind.2018.06.007.
  • 11. Guo Z Y, Zhou D, Zhou Q, Meia S, Zeng S, Yu D, Chen J. A hybrid method for evaluation of maintainability towards a design process using virtual reality. Computers & Industrial Engineering, 2020, 140(1): 106227 , https://doi.org/10.1016/j.cie.2019.106227.
  • 12. Guo Z Y, Zhou D, Zhou D D, Zhang X, Geng J, et al. Applications of virtual reality in maintenance during the industrial product lifecycle: A systematic review. Journal of Manufacturing Systems, 2020, 56, 525-538, https://doi.org/10.1016/j.jmsy.2020.07.007.
  • 13. Hao H J, Wang M L, Xu M, et al. Adaptive weighted data fusion of Muti-sensor based on fuzzy preference relation, IEEE International Conference on Information and Automation, 2016: 195-199, https://doi.org/10.1109/ICInfA.2016.7831821.
  • 14. Kline M B. Suitability of the lognormal distribution for corrective maintenance repair times. Reliability Engineering, 1984, 9(2): 65-80, https://doi.org/10.1016/0143-8174(84)90041-6.
  • 15. Lu Z, Zhou J, Li N X. Maintainability fuzzy evaluation based on maintenance task virtual simulation for aircraft system. Maintenance and Reliability, 2015, 17 (4): 504–512, http://dx.doi.org/10.17531/ein.2015.4.4.
  • 16. Lu Z, Liu J, Li D, Liang X H. Maintenance Process Simulation Based Maintainability Evaluation by Using Stochastic Colored Petri Net. Applied Sciences, 2019, 9(16), 3262, https://doi.org/10.3390/app9163262.
  • 17. Luo X, Ge Z X, Zhang S G, Yang Y M. A method for the maintainability evaluation at design stage using maintainability design attributes, Reliability Engineering & System Safety, 2021, 210: 107535, https://doi.org/10.1016/j.ress.2021.107535.
  • 18. MA Z, Ben-Tzvi P. RML glove--an exoskeleton glove mechanism with haptics feedback. IEEE/ASME Transactions on Mechatronics, 2015, 20(2), 641–652, https://doi.org/10.1109/tmech.2014.2305842.
  • 19. Miao Q, Liu L, Yuan F, Michael P. Complex system maintainability verification with limited samples, Microelectronics Reliability, 2011, 51(2): 294-299, https://doi.org/10.1016/j.microrel.2010.09.012.
  • 20. MIL-STD-471A. Maintainability verification/demonstration/evaluation; 1973.
  • 21. Osafo-Yeboah B, Jiang S, Delpish R, Jiang Z, Ntuen C. Empirical study to investigate the range of force feedback necessary for best operator performance in a haptic controlled excavator interface. International Journal of Industrial Ergonomics, 2013, 43(3), 197–202, https://doi.org/10.1016/j.ergon.2013.02.005.
  • 22. Overtoom E M, Horeman T, Schreuder H W R. Haptic Feedback, Force Feedback, and Force-Sensing in Simulation Training for Laparoscopy: A Systematic Overview. Journal Of Surgical Education, 2019, 76 (1) , 242-261, https://doi.org/10.1016/j.jsurg.2018.06.008.
  • 23. Pedro M D L, Vicente G P, Luis B M, Adolfo C M. A practical method for the maintainability assessment in industrial devices using indicators and specific attributes. Reliability Engineering & System Safety, 2012, 100, 84–92, https://doi.org/10.1016/j.ress.2011.12.018.
  • 24. Peng G L, Yu H, Liu X H, Jiang Y, Xu H. A desktop virtual reality-based integrated system for complex product maintainability design and verification, Assembly Automation, 2010, 30(4): 333-344(12), https://doi.org/10.1108/01445151011075799.
  • 25. Retterer B L, Kowalski R A. Maintainability: A historical perspective, IEEE Transactions on Reliability, 1984, R-33(1): 56-61, https://doi.org/10.1109/TR.1984.6448275.
  • 26. Sagardia M, Hertkorn K, Hulin T, Schätzle S, et al., VR-OOS: The DLR’s virtual reality simulator for telerobotic on-orbit servicing with haptic feedback, IEEE Aerospace Conference, 2015: 1-17, https://doi.org/10.1109/AERO.2015.7119040.
  • 27. Shao B C. Research on mobile robot training and control technology based on force feedback and virtual reality, Southeast University, 2021, https://doi.org/10.27014/d.cnki.gdnau.2021.001716.
  • 28. Seemann W, Stelzner G, Simonidis C. Correction of motion capture data with respect to kinematic data consistency for inverse dynamic analysis. ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, 2005:187-194, https://doi.org/10.1115/DETC2005-84964.
  • 29. Tu M X, Lv C, Wang M H, Zhou D, Xu Y L, Wan B L, He W X. Maintainability analysis and evaluation of flexible cables based on DELMIA. Transactions of the Canadian Society for Mechanical Engineering. 40(5): 995-1005, https://doi.org/10.1139/tcsme-2016-0082.
  • 30. Wang X, Di P. Testability Evaluation Method of Equipment Based on Data Fusion for Virtual and Real Test Data Fusion. Ship Electronic Engineering, 2021, 41(06):131-134.
  • 31. Wu Z Y, Hao J P. A Maintenance Task Similarity-Based Prior Elicitation Method for Bayesian Maintainability Demonstration. Mathematical Problems in Engineering, 2020, 1–19, https://doi.org/10.1155/2020/2730691.
  • 32. Yang X, Su W, Deng J, Jin X, Tan G, Pan Z. Real-virtual fusion model for traffic animation. Computer Animation and Virtual Worlds, 2016, 28(6), e1740, https://doi.org/10.1002/cav.1740.
  • 33. Zhang L, Brunnett G, Rusdorf S. Real-time human motion capture with simple marker sets and monocular video. Journal of Virtual Reality and Broadcasting, 2011, 8(1), https://doi.org/10.20385/1860-2037/8.2011.1.
  • 34. Zhu W Y, Zhou S Y. 3D Reconstruction Method of Virtual and Real Fusion Based on Machine Learning, Mathematical Problems in Engineering, 2022, 2022, 1-11, https://doi.org/10.1155/2022/7158504.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d12c6359-81e7-4442-9a62-c487b1e964f3
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