Reliability predictions are one of the most common forms of reliability analysis. Reliability predictions predict the failure rate of components and overall system reliability. These predictions are used to evaluate design feasibility, compare design alternatives, identify potential failure areas, trade-off system design factor, and tract reliability improvement.
The complexity on the probability of mission success can be evaluated by performing a reliability prediction analysis. Results from the analysis could determine a need for redundant systems, back-up systems, subsystems, assemblies, or component parts.
MIL-HDBK-217 (Electronics Reliability Prediction), Bellcore/Telcordia (Electronics Reliability Prediction) and NSWC (Mechanical Reliability Prediction) provide failure rate and MTBF (Mean Time Between Failures) data for electronic and mechanical parts and equipment. A reliability prediction can also assist in evaluating the significance of reported failures.
Ultimately, the results obtained by performing a reliability prediction analysis could be useful when conducting further analyses such as a FMEA (Failure Modes, Effects and Analysis), RBD (Reliability Block Diagram) or a Fault Tree analysis(FTA). The reliability predictions are used to evaluate the probabilities of failure events described in these alternate failure analysis models.
We provided failure Rate and MTBF using MIL-HDBK-217F and evaluated reliability using RBD, FMEA, FTA for YGN 5,6 Control Element Drive Mechanism Control system, KAERI FPGA Controller, HONAM Train Maintenance system & Local Control Console and HANARO Control Computer System.
Recently has been focusing on the RCA (Root Cause Analysis) to mitigate and eliminate underlying fundamental failure and process down time by exploiting the technologies such as Event and Causal Factor Charting, 5 Whys, FTA and other ancillary methods. Also exchanging information with oversea companies.