Heart Valve Component Fatigue Demonstration Testing Attribute and Variables Demonstration Test Planning and Sample Size Determination

TL;DR

This paper discusses planning and sample size determination for fatigue testing of heart valve components, comparing attribute and variables approaches to ensure reliable performance and fracture understanding.

Contribution

It introduces test planning methods for both attribute and variables approaches, including sample size calculations and risk management strategies.

Findings

Sample size formulas for attribute testing.

Simulation-based strain level determination for variables testing.

Complementary fracture testing enhances reliability understanding.

Abstract

The fatigue demonstration test program may be carried out using either the attribute or variables approach. For either approach, a robust test program fulfills the objective of unambiguously demonstrating reliability, as well as, demonstrating understanding of when and how the components could fracture. To this end, it is recommended that a robust test program should aim to produce data for both surviving units and fractured units. Test planning is needed to determine sample sizes and test levels of strain to prove that the component meets the target reliability. The two risks that must be addressed by the test plan are 1) the risk that an unreliable component will pass the test, i.e., Type 1 error, or 2) the risk of a test failing for a sufficiently reliable component, i.e., Type 2 error. Test planning methods are described to design a test plan that efficiently meets these objectives for attribute and variable approaches. The attribute approach offers the advantage of straight forward planning and a simple output. Sample size is calculated using a well-known formula. Testing to fracture is recommended to complement the traditional attribute testing approach to identify the test failure modes. The variables approach is more complex to plan and analyze, however it enables a greater understanding of reliability. Sample sizes and test levels of strain are determined using a simulated data strategy to provide a clear picture of possible outcomes of the test.