TY - JOUR
T1 - Economic allocation of test times for subsystem-level reliability growth testing
AU - Coit, David W.
PY - 1998
Y1 - 1998
N2 - In the development of new electronic systems the planning of reliability growth tests has become both more critical and more difficult as available testing budgets have diminished. Previously, system designers were able to plan and implement relatively lengthy reliability growth test plans to assist in the development of reliable systems. A new method is presented to allocate subsystem reliability growth test time in order to maximize the system mean time between failure (MTBF) or system reliability when designers are confronted with limited testing resources. For certain problems, the algorithm yields the same results as competing approaches to the problem but with significantly fewer required iterations. More significantly, the new algorithm applies to a larger problem domain compared to analogous algorithms. Much more realistic formulations of the problem can now be solved optimally. The algorithm is based on objective function gradient information projected onto a feasible region that consists of candidate test plans given the testing budget constraint. The algorithm is demonstrated on several examples with superior results.
AB - In the development of new electronic systems the planning of reliability growth tests has become both more critical and more difficult as available testing budgets have diminished. Previously, system designers were able to plan and implement relatively lengthy reliability growth test plans to assist in the development of reliable systems. A new method is presented to allocate subsystem reliability growth test time in order to maximize the system mean time between failure (MTBF) or system reliability when designers are confronted with limited testing resources. For certain problems, the algorithm yields the same results as competing approaches to the problem but with significantly fewer required iterations. More significantly, the new algorithm applies to a larger problem domain compared to analogous algorithms. Much more realistic formulations of the problem can now be solved optimally. The algorithm is based on objective function gradient information projected onto a feasible region that consists of candidate test plans given the testing budget constraint. The algorithm is demonstrated on several examples with superior results.
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U2 - https://doi.org/10.1023/A:1007580318576
DO - https://doi.org/10.1023/A:1007580318576
M3 - Article
VL - 30
SP - 1143
EP - 1151
JO - IIE Transactions (Institute of Industrial Engineers)
JF - IIE Transactions (Institute of Industrial Engineers)
SN - 0740-817X
IS - 12
ER -