Standard Guide for Reliability Demonstration Testing

Importancia y uso:

4.1 Reliability demonstration testing is a methodology for qualifying or validating a product’s performance capability. Demonstration methods are useful for components, devices, assemblies, materials, processes, and systems. Many industries require demonstration testing either for new product development and product introduction, in validating a change to an existing product or as part of an audit. Test plans generally try to answer the questions, “How long will a product last?” or “What is its reliability?”, under stated conditions at some specific time. When time is being used as a life variable, it must be cast in some kind of “time” units. Typical time units are hours (or minutes), cycles of usage, calendar time or some variation of these. In certain cases, “time” can be accelerated in order to reduce a plan’s completion time. In the automotive industry mileage may be used as the time variable. Certain means of accelerating tests involve the use of increased power, voltage, mechanical load, humidity, vibration, or temperature (often in the form of thermal cycling).

4.2 Two fundamental objectives in reliability test planning are: (a) demonstrating that a product meets a specific life requirement, and (b) demonstrating what a product can do – its life capability. In the first case, a requirement is specified; in the second case a series of test results are used to state a result at the present time – its current capability. Both cases share similar inputs and outputs.

4.3 Often a life distribution model is specified such as the Weibull, the exponential, the lognormal or the normal distribution. In addition, for the specific distribution assumed, a parameter is typically assumed (or a range of values for a parameter). For example, in the Weibull case, the shape parameter, β, is assumed; in the lognormal case the scale parameter, σ, is assumed and in the normal case the standard deviation, σ, is assumed. In other cases, a non-parametric analysis can be used. Non-parametric cases typically require a larger sample size than parametric cases. This standard will discuss conditions under which distributions and associated parameters can be assumed.

4.4 Generally, a life requirement is cast as a mission time and associated reliability, for example, to demonstrate a reliability of 99% at time t=1000 hours of usage. In another case the requirement might be cast as a B_p life requirement, such as the B₅ life. For example, if B₅ = 10 000 cycles are specified, this means to demonstrate a reliability of 95 % at t = 10 000 cycles. Other life requirements might be a mean life, a median life (B₅₀) or a failure rate not to be exceeded at a specified time t. In other cases, the requirement might mean withstanding a load for some duration. Demonstration necessarily means to demonstrate with some statistical confidence. Thus, a confidence value is a standard input in any plan. Commonly used confidence values are 99 %, 95 %, 90 %, and 63.2 %.

4.4.1 When a requirement and a confidence value have been stated, a derived plan will determine a sample size, n, a test time, t, and a maximum number of failures, r, allowed by the plan. A test concludes and is successful if the n units tested result in not more than r failures by time t. In another scenario, the sample size, number of failures allowed and confidence value are first stated and the plan returns the test time requirement.

4.5 The “RC” nomenclature for specifying a test requirement is often used, where R stands for reliability and C for confidence. For example, to state a requirement of 2000 hours at R99C90 means that the requirement is to demonstrate 99 % reliability at 2000 hours with 90 % confidence. Alternatively, this also means to demonstrate a B₁ life of 2000 hours with 90 % confidence.

4.6 This guide considers, the Weibull, lognormal and normal parametric cases as well as the basic non-parametric case for attribute reliability. The common exponential case is a Weibull distribution with assumed shape parameter β = 1, but is considered as a separate case, distinct from the Weibull.

Subcomité:

E11.40

Volúmen:

14.01

Número ICS:

21.020 (Characteristics and design of machines, apparatus, equipment)

Palabras clave:

binomial distribution; demonstration testing; exponential distribution; failure mode; failure probability; lognormal distribution; non-parametric; normal distribution; Poisson distribution; reliability; test plan; Weibull distribution;