Forum: Reliability & Maintainability Questions and Answers

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Posted by: LE GALLIOT (david.le.galliot@sonovision-itep.fr )
Organization:SONOVISION-ITEP
Date posted: Mon Apr 23 10:58:21 US/Eastern 2007
Subject: Use of level confidence with NPRD-95
Message:
Hello,
I have a question about use of confidence level with NPRD-95: According to NPRD-91 and 95, "the natural logarithm of the observed failure rates is normally distributed with a sigma=1.5. This indicates that 68% of actual failure rates will be between 0.22 and 4.5 times the mean value". Or, if the parameters of the lognormal distribution are mu and sigma, then the median of the distribution is given by exp(mu) and the mean is given by exp(mu+0.5*sigma*sigma). This indicates, for sigma =1.5, that 56% of actual failure rates will be between 0.2 and 4.5 times the mean value and 68% of actual failure rates will be between 0.2 and 4.5 times the median value. I think that there is an error in NPRD-91 an NPRD-95: either it would be necessary to read “This indicates that 56% of actual failure rates will be between 0.22 and 4.5 times the mean value", either it would be necessary to read “This indicates that 68% of actual failure rates will be between 0.22 and 4.5 times the median value". Is anyone could tell me what really represent all listed failure rates on the both NPRD-91 an NPRD-95: the medians or the means of the lognormal distributions?

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Posted by: Sheila Prather (sheila.prather@ngc.com )
Organization:Northrop Grumman Corp
Date posted: Thu Apr 19 16:23:16 US/Eastern 2007
Subject: Mean Time Between Event vs. MTBF
Message:

I have a requirement to compute MTBE and Eavg. MTBE is the system prediction of the Mean Time Between Event, and Eavg is the predicted mean time to correct an Event. An Event is defined as either a Failure, a Maintenance Jam or and Operator Jam. Mean Time to Correct an Event (Eavg) is the average down time associated with an Event, including maintenance personnel or operator response time, time to diagnose the problem, repair or jam clearing time, and restart time. Can you recommend a methodology to get from predicted MTBF to MTBE? I would assume I would have to apply some factor to account for jams that might occur on the equipment, but not aware of any recommended factors.

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Posted by: Sean Cloarec (jmcloarec@systra.com )
Organization:SYSTRA
Date posted: Thu Mar 15 3:24:15 US/Eastern 2007
Subject: SSHA versus FMECA
Message:
Hello, I've always been a little confuse about the REAL difference between SSHA and FMECA. SSHA relates to "functional failures" and FMECA to "failures". There may be some confuse overlaps. I've seen SSHA that looks like FMECA.
Is there anybody having some clarification ???? Thanks in advance. Sean

Replies:
SSHA versus FMECA
David Dylis Thu Mar 15 8:58:25 US/Eastern 2007

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Posted by: Dabney
Date posted: Mon Mar 12 15:44:04 US/Eastern 2007
Subject: Reliability in a non-manufacturing environment
Message:
I am familiar with the concept of reliability and RCFA's when I worked for a manufacturing company. Now, I work in the services industry, and I have been given a project regarding this topic by my director, who has a manufacturing background as well. He would like me to apply basic reliability and an RCFA type methodology to our department. We do similar processes every quarter and year-end with occasional errors. Is anyone aware of any non-manufacturing reliability website or white papers?

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Posted by: Mary Priore (mpriore@alionscience.com )
Organization:System Reliability Center
Date posted: Mon Mar 5 18:25:21 US/Eastern 2007
Subject: MEMS Reliability Study
Message:
Microelectromechanical Systems (MEMS) represent a revolution in microfabrication, able to transform nearly every industry it touches. First associated with airbag sensors and ink jet heads, MEMS have now invaded applications as diverse as household robots and military munitions. They facilitate the creation of “smart” products, in which the computational ability of microelectronics is expanded with the perception and control capabilities of micron-sized sensors and actuators. Their unprecedented levels of functionality, reliability and sophistication, combined with the low costs of large-scale fabrication may put MEMS into every toy, appliance and consumer electronics product. Like all emerging technologies, however, MEMS has its share of challenges. In particular, the microscopic nature of MEMS reveals new failure mechanisms and modes not normally encountered in macro-scale components.
The System Reliability Center (SRC) is currently looking for reliability test, field experience, and failure mode/mechanism data on Microelectromechanical (MEMs) devices. The types of reliability data required include but are not limited to Qualification, Test, and Field Experience. Ideally the data should include the quantity of devices, number of failures, device hours and/or test/environment stresses imposed on the devices. SRC will provide a complimentary electronic PDF copy of the recent SRC publication, Microelectromechanical Systems (MEMs) (Sunada, 2005) to organizations providing MEMs reliability data that can used in a current research study. For more information on how to submit data, please contact mpriore@alionscience.com . The SRC realizes that it is very important for the reliability community to have access to accurate reliability data. We currently have over 100 active resources of reliability data. SRC is always investigating new sources of reliability and maintainability data for systems, equipment, assemblies, and components. Data of interest includes field failure, maintenance, and test data for commercial and military systems/components.

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