Abstract
In view of the abnormal, missing and multi-source reliability data, a complete reliability data processing method for civil machinery parts are formed. The abnormal data in the original reliability data set is screened out by Pauta criterion. The vacant data is supplemented by linear interpolation. Each subjective index weight of multi-source data is determined by analytic hierarchy process (AHP) and Euclidean distance. Each objective index weight is determined by the discrete degree of the index weight. The comprehensive reliability model about a certain mechanical part with multi-source data is established and validated to be practical through case analysis. The proposed method obtains more accurate assessment of part reliability, and is thus valuable for the safe operation and maintenance of civil aircraft.
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Acknowledgments
This work is supported by Research Program supported by National Natural Science Foundation of China (U1333119); National defense basic scientific research program of China (JCKY2013605B002); Civil Aircraft Special Foundation of Ministry of Industry and Information Technology (MJ-2017- J-91); Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17-0273); Fund of Shanghai Engineering Research Center of Civil Aircraft Health Monitoring (No. GCZX-2015-05).
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Recommended by Associate Editor Yongho Jeon
Yuanyuan Guo is currently a Ph.D. candidate of the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China. Her research interests include reliability system engineering, operational safety analysis and risk assessment of civil aircraft.
Youchao Sun is a Professor of the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interests focus on reliability engineering, risk assessment and airworthiness technology of civil aircraft.
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Guo, Y., Sun, Y., Li, L. et al. Reliability assessment for multi-source data of mechanical parts of civil aircraft based on the model. J Mech Sci Technol 33, 3205–3211 (2019). https://doi.org/10.1007/s12206-019-0615-4
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DOI: https://doi.org/10.1007/s12206-019-0615-4