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Reliability engineering videos

Reliability engineering videos

Reliability Growth Analysis: Why, When and How It Is Applied

An overview of the Reliability Growth methodology is presented, aiming to answer the following questions:
  • What benefits does Reliability Growth bring to ensure high system reliability?
  • When can Reliability Growth be applied? (Developmental testing and fielded repairable systems will also be discussed.)
  • How can you plan good Reliability Growth Tests, and which Reliability Growth models should you use?

Originally presented on March 9, 2021

Notes from the presenter

Continuous and Discrete Reliability Growth

Presented webinar focuses on times-to-failure data also known as continuous data for repairable systems. There are many cases however, where the data is not continuous but discrete and the system is non-repairable. These cases involve recording data from a test where a unit is tested with only 2 outcomes: success or failure. Crow – AMSAA, Crow - Extended and Crow - Extended Continues Evaluation models can be applied to this type of data as well. Additionally, RGA module allows to utilize the following models for the discrete data: Standard Gompertz, Lloyd - Lipow, Modified Gompertz, Logistic.


Integrated Reliability Growth program

Reliability growth testing can be integrated into existing testing programs such as operational testing, safety testing or any other testing that is involved with the operation of that specific product. This type of reliability growth program is relatively cost-effective because the additional cost is minimal. The data from accelerated life test can also be used providing that the acceleration factor is known.


Reliability Growth planning and input parameters

Reliability growth planning is the essential part of reliability growth program. It allows to estimate the time to achieve specific reliability target and number of systems in test. It is important to understand that initial planning is based on the set of parameters which may or may not be known. However, these initial parameters can be updated during the reliability growth program based on the collected data and observed reliability growth.

  • Discovery beta: Indicates how often new failure modes are discovered during the test. The value may come from the initial demonstration test for the prototype or first phase of testing. High value, Beta > 0.8 indicates that not much attention was given to reliability during design stage and most likely it is too early for reliability growth program. Low value: Beta < 0.6 indicates that there are only few main failure modes.
  • Management strategy ratio: Portion of failure modes which will be addressed using some corrective actions. A maximum MSR = 0.95 is recommended.
  • Effectiveness factor: Portion of failure mode removed after corrective action. Historically, the average EF for an entire system is about 0.7 but can obviously vary depending on the system.
  • Growth potential design margin: how much can we possibly improve our system. Typical range is 1.2 < GPDM < 1.67. Values below 1.2 indicate that very long time is needed to improve the system.

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