Sampling for assurance of future reliability

Abstract

Ensuring measurement trueness, compliance with regulations and conformity with standards are key tasks in metrology which are often considered at the time of an inspection. Current practice does not always verify quality after or between inspections, calibrations, laboratory comparisons, conformity assessments, etc. Statistical models describing behavior over time may ensure reliability, i.e. they may give the probability of functioning, compliance or survival until some future point in time.It may not always be possible or economic to inspect a whole population of measuring devices or other units. Selecting a subset of the population according to statistical sampling plans and inspecting only these, allows conclusions about the quality of the whole population with a certain confidence.Combining these issues of sampling and aging, raises questions such as: How many devices need to be inspected, and at least how many of them must conform, so that one can be sure, that more than of the population will comply until the next inspection? This research is to raise awareness and offer a simple answer to such time- and sample-based quality statements in metrology and beyond.Reliability demonstration methods, such as the prevailing Weibull binomial model, quantify the confidence in future reliability on the basis of a sample. We adapt the binomial model to be applicable to sampling without replacement and simplify the Weibull model so that sampling plans may be determined on the basis of existing ISO standards. Provided the model is suitable, no additional information and no software are needed; and yet, the consumer is protected against future failure.We establish new sampling plans for utility meter surveillance, which are required by a recent modification of German law. These sampling plans are given in similar tables to the previous ones, which demonstrates their suitability for everyday use.