Метод учета влияния системы менеджмента надежности предприятия при расчетной оценке показателей безотказности электронных средств
The current period of creation of the electronic equipment of a responsible and special purpose is characterized by universal introduction of the Quality Management Systems at the enterprises developers and producers of electronic equipment. Quality Management System and its component – Dependability Management System are aimed at providing the guaranteed level of indicators of quality (including and dependability indicators).
In the reliability prediction method recommended by the Russian standards influence of procedures of Dependability Management System is considered with the help of «Coefficient Quality Production Equipment» (КА). This coefficient considers and reflects an average difference in failure rate of elements in the equipment developed and manufactured on requirements of various standard documentation (1 – for a complex of the standards «Moroz …» or 0,2 – for the situation «RK-…»).
However, such approach to forecasting reliability prediction of electronic equipment at the early design stages, based on use of average statistical data rather approximate. It doesn’t consider neither features of Dependability Management System of the concrete enterprise, nor completeness of the Support Reliability Program when developing.
Therefore more adequate approach to an assessment of value of the coefficient КА realized in methodology 217PlusTM is represented. According to this methodology when forecasting value of the coefficient КА are used not only statistical estimates, but also an expert assessment of Dependability Management System effectiveness during the developing and production of the equipment.
The article discusses all the features of application of methodology 217PlusTM for an assessment of the coefficient КА: mathematical model of multiple-factor coefficient of quality of production, analysis of influence of its components on the general level and functional model of reliability prediction process.