ASQ RRD series webinar: Understanding Key Variables Impacting Reliability Demonstration Testing

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On Thu, Feb 10, Pankaj Shrivastava presented

ASQ RRD series webinar: Understanding Key Variables Impacting Reliability Demonstration Testing


Reliability tests are primarily used to detect underlying design-based wear-out failure mechanisms and latent production defects. A reliability test typically helps in achieving overall system reliability by providing opportunities for improving the design and production processes.
These tests need to be designed as a set of well-defined accelerated tests to meet the system reliability target and save product development time. Reliability demonstration test (RDT) are generally set up as a success test and no failures are expected in the test.
All test specimens are supposed to survive the designated amount of test time to demonstrate the minimum system reliability.
The conclusion of an RDT always leads to a comprehensive and meaningful reliability definition of the product.
RDT is typically required to demonstrate if the system has met its reliability targets at the end of development process.
Because of expensive test specimens and shorter product development time, a key step in designing RDT plan is to optimize the set of sample size and test time.
The optimization process may result in a test plan situated over the extremes of test time and sample size i.e. short test time with large sample size and long test time with small sample size.
Both of these categories of test plan are mathematically appropriate to satisfy the reliability target at a desired confidence.
However, there is a requirement of determining the minimum number of samples and the appropriate test time that is sufficient to precipitate the underlying failure mode.
This presentation provides a methodical approach of optimizing RDT by analyzing the impact of sample size, test time, desired confidence and acceleration factor.
RDT requires a consideration of underlying life distribution along with approximation of distribution shape parameter.
A twoparameter Weibull distribution.

Pankaj Shrivastava is a Reliability Technical Advisor in Halliburton Completion Tools based  in Carrollton, TX. He has over 17 years of experience in reliability engineering, design for reliability, maintenance modeling, and predictive analytics. He holds a MS degree in Reliability Engineering. He is an ASQ certified Six Sigma Black Belt and ASQ certified Reliability Engineer.  He has over 15 publications in international journals and conferences and holds 1 US patent. Prior to Halliburton, he has worked in General Electric Company (GE Energy) and Accenture Management Consulting.

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