Learn about the most widely accepted Reliability Prediction standards, including MIL-HDBK-217, Telcordia, 217Plus, China's GJB/z 299, and the NPRD and EPRD component databases, and how to choose which one to use. Telcordia (Bellcore) SR-332 issue 3. • NSWC handbook / procedure for mechanical equipment. •Question: Why isn’t the Telcordia predicted MTBF of a ruggedized notebook higher than the base platform? Telcordia TR-332 SR-332, Bellcore Electronic Component Reliability Prediction Software. Download a free demo now. Reliability Prediction Procedure for Electronic Equipment SR-332 Table of Contents vii Enterprise License Restrictions. See restrictions on title page.
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How Were Reliability Prediction Methods Developed? Reliability Predictions are often used in product design and development as part of reliability and quality continuous improvements efforts. To perform a reliability prediction analysis, a standard is employed. Each Reliability Prediction standard offers a set of mathematical formulas to model and calculate the failure rate of a variety of electromechanical components that make up a product or system. These equations were built by analyzing a huge amount of field data over a long period of time.
Statistical analysis was then used to determine the equations which best modeled the failure characteristics of the accumulated data. The variables used in the reliability calculation formulas to calculate component failure rates vary, but include data such as device ratings, temperatures, operating parameters, and environmental conditions. The clipse hell hath no fury zip. The result of a reliability prediction analysis is the predicted failure rate or Mean Time Between Failures (MTBF) of a product or system, and of its subsystems, components, and parts. Reliability Prediction’s historical roots are in the military and defense sector, but over the years have been adapted and broadened for use in a wide range of industries.
Essentially, the advantages afforded by reliability prediction analyses make it an important part of managing and maintaining reliability and quality objectives. This article provides an overview of the most commonly used reliability prediction standards.
How To Use Failure Rate Predictions to Improve Reliability Reliability Predictions offer a path to product improvement by supporting the ability to “design in” reliability. At the early design stage, Reliability Predictions enable you to perform an assessment of likely failure rate characteristics. By predicting failure rates, you can then make design changes as needed for areas of weakness. Reliability Predictions can also be used to evaluate design options by considering the reliability profiles of the various alternatives. This ability to perform design trade-off analysis with metric-based assessments empowers you to make the best decisions for your business.
What are the Primary Reliability Prediction Standards? Five of the most widely used Reliability Prediction standards for reliability analysis. The MIL-HDBK-217 Reliability Prediction Standard MIL-HDBK-217 is one of the most widely known Reliability Prediction standards. It was one of the first models developed, and many other reliability standards available today have their roots in MIL-HDBK-217.
MIL-HDBK-217’s official name is Military Handbook: Reliability Prediction of Electronic Equipment. It was originally developed and published for use by the Department of Defense. Over the years there have been many updates to the MIL-HDBK-217 document, which have resulted in the suffix designations in the document name: MIL-HDBK-217D and MIL-HDBK-217E Notice 1 for example. The current release of MIL-HDBK-217 is MIL-HDBK-217F Notice 2. MIL-HDBK-217 was one of the first models developed, and many other reliability standards available today have their roots in MIL-HDBK-217. There are two primary sections in the MIL-HDBK-217 standard: the Part Stress section and the Parts Count section. MIL-HDBK-217: Part Stress Section The Part Stress section leads off the document and includes a number of equations that predict the failure rate for a wide variety of electrical components.