BS IEC 61650-1997 可靠性数据分析技术.双恒定失效率和双恒定失效(事件)密度比较程序

标准号：BS IEC 61650-1997
中文标准名称：可靠性数据分析技术.双恒定失效率和双恒定失效(事件)密度比较程序
英文标准名称：Reliability data analysis techniques - Procedures for comparison of two constant failure rates and two constant failure (event) intensities
标准类型：K04
发布日期：1997/10/15 12:00:00
实施日期：1997/10/15 12:00:00
中国标准分类号：K04
国际标准分类号：03.120.01
适用范围：This International Standard specifies procedures to compare two observed– failure rates;– failure intensities;– rates/intensities of relevant events.The procedures are used to determine whether an apparent difference between the two sets ofobservations can be considered statistically significant.It is assumed that the time intervals to/between the failures (events) are independent andidentically exponentially distributed during the observation period (that is, the accumulatedrelevant test time).NOTE – This assumption implies that the failure rate/intensity is constant.It is furthermore assumed that there are technical or other reasons to believe that a difference(either an improvement or deterioration) might exist between the observed reliabilitycharacteristic of the two sets of items under comparison. Some examples of typicalapplications are described in 5.4.The methods are designed as hypothesis tests which state, with a specified risk (thesignificance level), whether the two series of observations belong to the same population or thesame process, that is they have the same true mean value.NOTE – Failure rate, which is relevant to non-repaired items, is associated with a distribution of times to failure.Failure intensity, which is relevant to repaired items only, is associated with a point process describing a sequenceof events, for example times between failures on a time axis.The procedures are not restricted to comparison of failure rate/intensity, but can be applied toobservations of two series of any relevant events, provided the above assumptions are valid.NOTE – The two series of observations may be of items from the same population, or the same item underdifferent conditions (for example environment and load), or just comparable series of events (for example caraccidents on a road).Numerical methods and a graphical procedure are prescribed. The observation periodsrelevant to the two series do not need to be equal, but if they are, the methods are very simple.