Browse > Article
http://dx.doi.org/10.11627/jkise.2013.36.4.100

A Study on Reliability Growth through Failure Analysis by Operational Data of Avionic Equipments  

Jo, In-Tak (Korea Aerospace Industries, LTD.)
Lee, Sang-Cheon (Division of Industrial Systems Engineering, ERI, Gyeongsang National University)
Park, Jong Hun (Department of Business Administration, Catholic University of Daegu)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.36, no.4, 2013 , pp. 100-108 More about this Journal
Abstract
In aerospace industry, MTBF (Mean Time Between Failure) and MFTBF (Mean Flight Time Between Failure) are generally used for reliability analysis. So far, especially to Korean military aircraft, MFTBF of avionic equipments is predicted by MIL-HDBK-217 and MIL-HDBK-338, however, the predicted MFTBF by military standard has a wide discrepancy to that of real-world operation, which leads to overstock and increase operation cost. This study analyzes operational data of avionic equipments. Operational MFTBF, which is calculated from operational data, is compared with predicted MFTBF calculated conventionally by military standard. In addition, failure rate trend is investigated to verify reliability growth in operational data, the investigation shows that failure rate curve from operational data has somewhat pattern with decreased failure rate and constant failure rate.
Keywords
Aerospace Reliability; Failure Rate Trend; MFTBF; MIL-HDBK-217F; Reliability Growth;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Raghuram R, HCL, Challenges in Reliability Prediction of Aircraft Subsystems, 2008.
2 Smith, S.A. and Oren, S.S., Reliability Growth of Repairable Systems. Naval Research Logistics Quarterly, 1980, Vol. 27, No. 4, p 39-547.
3 Jo, I.-T., Lee, S.-C., and Kim, Y.-H., A Study on Reliability Prediction Comparison of Aero Space Electronic Equipments. IE Interface, 2012, Vol. 25, No. 4, 472- 479.   DOI   ScienceOn
4 Crow, L.H., Planning a Reliability Growth Program Utilizing Historical Data. Reliability and Maintainability Symposium, January, 2011.
5 Duane, J.T., Learning curee approach to reliability monitoring. IEEE Transactions on Aerospace, 1964, Vol. 2, No. 2, 563-566.   DOI   ScienceOn
6 Jeon, T.-B., An Overview on the Emergence of the Reliability Prediction Methodology $217PLUS^{TM}$. Journal of Industrial Technology, Kangwon Natl, Univ. Korea, 2009, Vol. 29, No. A, p 28-36.
7 Jung, W., Application of Reliability Technology in Products Design and Development, Proceedings of 2005 Conferences(Spring) on The Korea Industrial and Systems Engineering, 2005.
8 Kim, E.-J, Won, J.-H., Choi, J.-H., and Kim, T.-G., A Study on Reliability Assessment of Aircraft Structural Parts. Journal of the Korean Society for Aviation and Aeronautics, 2010, Vol. 18, No. 4, p 38-43.
9 Lee, Y.-E., Choi, J.-Y., and Kang, J.-Y., Mission Reliability Analysis and Prediction for Aircraft System based on FA-50 Aircraft Development Experience. Proceedings of 2010 Conference(Fall) on The Korean Society for Aeronautical and Space Sciences, 2010.
10 Lee, Y.-E., Kim, K.-Y., Lee, K.-H, Kim, Y.-H., and Jung, Y.-M., Reliability Growth Analysis for KA-1 Aircraft- Based on Duane and Crow-AMSAA Model, Proceedings of 2010 Conference(Spring) on The Korean Society for Aeronautical and Space Sciences, 2010.
11 MIL-STD-756B, Reliability Modeling and Prediction, 1981.
12 MIL-STD-785B, Reliability Program for Systems and Equipment Development and Production, 1980.
13 MIL-HDBK-217F Notice 2, Military Handbook, Reliability Prediction of Electronic Equipment, Department of Defense, 1995.
14 MIL-HDBK-338B, Electronic Reliability Design Handbook, 1998.
15 MIL-STD-1635, Reliability Growth Testing, 1978.
16 Moasoft, A Guide Book for Reliability Prediction, 2002.