{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T11:00:15Z","timestamp":1784199615013,"version":"3.55.0"},"reference-count":29,"publisher":"Emerald","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["72071111"],"award-info":[{"award-number":["72071111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"crossref","award":["G2021181014L"],"award-info":[{"award-number":["G2021181014L"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,17]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The purpose of this paper is to reveal the bottleneck of reliability growth for carrier rockets by establishing different grey system models to simulate and predict the success rate of rocket launches worldwide. And to raise awareness of the importance of enhancing rocket launch reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>An overview of the development history of launch vehicles and their reliability challenges was presented at first. Then, a briefly introductions to the launch mission assurance process of the US National Security Space Launch (NSSL) program as well as the reliability management system of China\u2019s Long March 7 launch vehicle was given. Based on global, the US and Chinese launch success rate data from 2018 to 2024, four grey prediction models of the EGM, ODGM, EDGM and DGM were established, yielding high-precision simulation and forecasting results. The study reveals that the improvement of launch vehicle reliability is facing significant bottleneck constraints. Finally, an innovative approach to address the existing challenges in launch vehicle reliability analysis and evaluation is proposed in this paper.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>The simulation and prediction outcome showed that the global launch success rate had a slow upward trend. The reliability growth of launch vehicles is severely constrained by bottlenecks. Big data technologies, along with uncertainty system analysis methods based on diverse perspectives \u2013 such as probability statistics, fuzzy mathematics, grey system theory and rough set theory \u2013 as well as innovations in sequence operators, spectral analysis and intelligent algorithms, have laid a solid foundation for effectively integrating complex uncertain data and breaking through the bottlenecks of reliability modeling. The conditions are increasingly ripe for exploring new approaches and methodologies in launch vehicle reliability analysis and evaluation by comprehensively leveraging big data technologies, multiple uncertainty system analysis methods, sequence operators, spectral analysis and intelligent algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Research limitations\/implications<\/jats:title>\n                    <jats:p>The limitation of this research is that it revealed the bottleneck of reliability growth for carrier rockets and proposed a novel approach to overcome the bottleneck constraints in launch vehicle reliability analysis and evaluation, which integrates big data technologies with diverse uncertainty system analysis methods (including probability statistics, fuzzy mathematics, grey system theory and rough set theory) as well as complex uncertain data fusion techniques such as sequence operators, spectral analysis and intelligent algorithms. However, the specific methodological approaches to break through the bottleneck constraints in launch vehicle reliability growth remain to be further investigated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical implications<\/jats:title>\n                    <jats:p>Manufacturers use reliability growth tests to iteratively improve launch vehicle reliability and performance to predetermined levels through cycles such as \u201cexposing defects\u2014analysing causes\u2014improving designs, processes, or operations.\u201d However, when the data does not meet the modeling conditions of traditional reliability growth models, people often adopt some \u201cflexible\u201d approach, such as using simulated data or borrowing relevant data from similar equipment to \u201cpiece together\u201d data, which may bury hidden dangers in launch vehicle reliability. There is an urgent need to explore new models and methods. The novel idea proposed in this paper has the potential to significantly improve the quality and reliability of launch vehicle in smart manufacturing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This paper proposes a novel approach to overcome the bottleneck constraints in launch vehicle reliability analysis and evaluation, which integrates big data technologies with diverse uncertainty system analysis methods (including probability statistics, fuzzy mathematics, grey system theory and rough set theory) as well as complex uncertain data fusion techniques such as sequence operators, spectral analysis and intelligent algorithms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/gs-07-2025-0099","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T19:00:43Z","timestamp":1765047643000},"page":"346-362","source":"Crossref","is-referenced-by-count":1,"title":["Prediction and analysis of global launch vehicle success rates based\u00a0on\u00a0grey system models"],"prefix":"10.1108","volume":"16","author":[{"given":"Wei","family":"Tang","sequence":"first","affiliation":[{"name":"Center for Grey Systems Studies and School of Management, Northwestern Polytechnical University , ,","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Hu","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology, Beijing Aerospace Automatic Control Research Institute , ,","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Grey Systems Studies and School of Management, Northwestern Polytechnical University , ,","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dahai","family":"Li","sequence":"additional","affiliation":[{"name":"China Academy of Aerospace Liquid Propulsion Technology , ,","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyong","family":"Wang","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology, Beijing Aerospace Automatic Control Research Institute , ,","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"issue":"12","key":"2026031700413730800_ref001","first-page":"30","article-title":"Fuzzy control for reliability growth testing based on grey system prediction","volume":"27","author":"Chen","year":"2008","journal-title":"Ordnance Industry Automation"},{"key":"2026031700413730800_ref002","volume-title":"Reliability Growth Estimation From Failure and Time Truncated Testing","author":"Crow","year":"1974"},{"key":"2026031700413730800_ref003","first-page":"84","article-title":"Reliability growth projection from delayed fixed","author":"Crow","year":"1983"},{"key":"2026031700413730800_ref004","first-page":"978","article-title":"The extended continuous evaluation reliability growth model","author":"Crow","year":"2010"},{"key":"2026031700413730800_ref005","volume-title":"Grey Control System","author":"Deng","year":"1985"},{"key":"2026031700413730800_ref006","article-title":"MIL-HDBK-189","author":"Department of Defense, US","year":"1981"},{"key":"2026031700413730800_ref007","article-title":"Technical information series report","author":"Duane","year":"1962"},{"key":"2026031700413730800_ref008","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1109\/rams.2005.1408374","article-title":"AMSAA maturity projection model based on stein estimation","author":"Ellner","year":"2005"},{"key":"2026031700413730800_ref009","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/rams.1995.513243","article-title":"AMSAA maturity projection model","author":"Ellner","year":"1995"},{"issue":"3","key":"2026031700413730800_ref010","first-page":"285","article-title":"Review of \u20182 + 9 + 2\u2019 Reliability engineering of LM-7 carrier rocket","volume":"23","author":"Fan","year":"2017","journal-title":"Manned Spaceflight"},{"issue":"4","key":"2026031700413730800_ref011","doi-asserted-by":"publisher","first-page":"188","DOI":"10.3901\/jme.2012.04.188","article-title":"Reliability growth prediction of Army Materiel system analysis activity model based on conditional distribution","volume":"48","author":"Guo","year":"2012","journal-title":"Journal of Mechanical Engineering"},{"key":"2026031700413730800_ref012","article-title":"IEC61014. 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