RGTMC를 이용한 기동화력장비 신뢰도 성장분석 사례 연구
Reliability Growth Analysis Using RGTMC: Armed Vehicle Case
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This case study deals with the reliability growth analysis for an armed vehicle using Crow-AMSAA RGTMC (Reliability Growth Tracking Model-Continuous). An armed vehicle is equipped with 7 major modules with a CBR(Chemical, Biological and Radiological) detection function. The failure data of these 7 modules are collected during the test and evaluation phases such as D'T(Developmental Test) and OT(Operational Test). For the reliability growth analysis, the operational test phase is divided into OT 1 and OT 2 due to the change of failure intensity. The collected failure data is also screened to eliminate some inaccurate and inappropriate data. The results of reliability growth analysis for an armed vehicle show that there is a significant reliability growth during Operational Test 2, and that DT and OT 1 does not give any sign of reliability growth during tests. On the contrary, the estimated reliability decreases, which implies that there may be more failures during the early test phases compared to the test phases later on.
Abstract
This case study deals with the reliability growth analysis for an armed vehicle using Crow-AMSAA RGTMC (Reliability Growth Tracking Model-Continuous). An armed vehicle is equipped with 7 major modules with a CBR(Chemical, Biological and Radiological) detection function. The failure data of these 7 modules are collected during the test and evaluation phases such as D'T(Developmental Test) and OT(Operational Test). For the reliability growth analysis, the operational test phase is divided into OT 1 and OT 2 due to the change of failure intensity. The collected failure data is also screened to eliminate some inaccurate and inappropriate data. The results of reliability growth analysis for an armed vehicle show that there is a significant reliability growth during Operational Test 2, and that DT and OT 1 does not give any sign of reliability growth during tests. On the contrary, the estimated reliability decreases, which implies that there may be more failures during the early test phases compared to the test phases later on.