Reliability Data Collection and Analysis Resources
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The 2016 Edition of the Nonelectronic Parts Reliability Data publication (NPRD-2016) presents field failure rate data on a wide variety of electrical assemblies and electromechanical/mechanical parts and assemblies. Compared to its predecessor NPRD-2011 publication, NPRD-2016 adds 138,000 new parts and over 370 billion part hours, representing approximately a 400% increase in content. The expanded part types and data in NPRD-2016 cover ground, airborne and naval environments.
The Electronic Parts Reliability Data (EPRD) publication contains field failure rate data for commercial and military electronic components for use in reliability analyses. Component types include integrated circuits, discrete semiconductors (diodes, transistors, optoelectronic devices), resistors, capacitors, and inductors/transformers.
This databook contains field failure mode and mechanism distribution data on a variety of electrical, mechanical, and electromechanical parts and assemblies. This data can be used to assist in the performance of reliability analyses and assessments such as Failure Modes, Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA).
The Reliability Online Automated Databook System (ROADS) is a subscription that provides customized access to all the Quanterion Databooks: Nonelectronic Parts Reliability Data (NPRD), Electronic Parts Reliability Data (EPRD) and/or Failure Modes / Mechanisms Distribution (FMD).
Tools and Resources
This fifth toolkit in the popular series developed by Quanterion reflects the updated focus in RMQSI. Several of the same Quanterion staff members have been involved in all five titles in the series. Over 30 new topics have been added, and several others updated, with a minimum removal of topics from the “System Reliability Toolkit.” In addition, Appendices have been added stressing the practical application of the document’s practices throughout a program’s life.
The Warranty Calculator planning and analysis tool provides automated calculations and comparisons for ten different warranty types in common use. The tool also includes data, information and references that provide further guidance for over 30 additional warranty types.
The “Reliability Toolkit: Commercial Practices Edition” helps both the commercial and military sectors deal with developing and manufacturing reliable products in today’s competitive markets. Over 80 topics, representing every aspect of a product’s reliability over its life cycle, have been well received by thousands of Toolkit users to date.
NOTE: The “Reliability Toolkit: Commercial Practices Edition” was published in 1995. It has been updated twice since publication with the latest version, “System Reliability Toolkit-V”.
The purpose of this publication is to provide illustrative examples of the more common mathematical calculations and statistical techniques utilized by reliability engineers in the practical performance of their daily activities. It is intended to be used as a companion to the RIAC “System Reliability Toolkit,” as the foundations of all of the techniques illustrated in this publication are discussed.
The purpose of this publication is to provide guidance on modeling techniques that can be used to quantify the reliability of a product or system. There are many ways in which this can be accomplished, depending on the product or system and the type of information that is available, or practical to obtain. It reviews the possible approaches, summarizes their advantages and disadvantages, and provides guidance on selecting a methodology based on the specific goals and constraints of the analyst.
This document is specifically targeted to address the competing approaches to part reliability predictions, including statistical analysis of relevant failure data, physics of failure modeling, empirical failure models and data, and other less common but acceptable methods.
The statistical analysis of an item’s failure data is widely regarded as one of the most accurate techniques for assessing its reliability in a specific application or environment. The Weibull distribution is especially useful because of its ability to characterize a wide range of potential data trends. More specifically, it can be fit to a dataset exhibiting an increasing, constant or decreasing failure rate, a unique feature that separates the Weibull distribution from its counterparts.
To learn more about reliability data collection and analysis, visit Quanterion’s associated reliability data collection and analysis informational page.
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