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Predict and improve equipment reliability

The Reliability Prediction module of Reliability Workbench™ delivers rich insights to help you easily calculate and visualize failure rates for a set of components under given conditions, so you can prioritize equipment maintenance based on quantitative data and analytics.

This tool makes it easy for you to:

  • Predict whether a design will meet reliability objectives and identify potential issues early in development.
  • Compare system design alternatives
  • Consider stress factors that cause significant impact to system performance
  • Prioritize equipment maintenance based on quantitative data and analytics

The prediction module provides you with a powerful visual interface through which you can select components and define the conditions in which they operate, such as the temperature or environmental conditions. The tool automatically calculates the Mean Time Between Failure (MTBF) rate as defined by a selected industry standard and provides you with the results.

Features

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Powerful visual interface

Easy-to-read graphs display variations in predicted failure rates, showing ambient temperature, stress and environment settings for an entire system or individual blocks and components.

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Make rapid changes

Cut, copy and paste in the diagram to make rapid changes. Commonly used components can be entered once in a library and then pulled into the project tree each time the component is used.

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Easily import data

Information can be imported from Excel, Access, SQL Server, Oracle and other formats, with powerful and customizable reporting.

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Calculate MTBF rate

The tool automatically calculates the Mean Time Between Failure (MTBF) rate as defined by a selected industry standard. With the results in hand, you can make faster design decisions. 

Supported Standards

FIDES arrow icon

The FIDES reliability prediction standard was produced by companies in the FIDES Group, under the supervision of the Direction générale de l’armement (DGA). Within the Reliability Workbench Prediction module, use it to make a realistic evaluation of the reliability of electronic products including those that encounter severe or non-aggressive environments. The FIDES Guide 2009 Edition A considers:

  • The existence of models both for electrical, electronic and electromagnetic components, and for electronic boards or some sub-assemblies
  • Electrical, mechanical and thermal over-stresses
  • Failures related to the development, production, operation and maintenance processes

Use it to easily modify component parameters and system phase profiles and instantly view the impact these changes have on the system reliability.


GJB/Z 299B and 299C arrow icon

These are Chinese standards for the reliability prediction of electronic components, which use the part stress method to predict the effects of environment, quality, electrical and thermal stress on component failure rates. The standard is fully implemented, including the hybrid component.

 


IEC TR 62380 (RDF 2000) arrow icon

Formerly called the RDF 2000 (UTE C 80-810) module, this is a powerful reliability prediction program based on the French telecommunications standard IEC TR 62380 Edition 1. Use it to:

  • Predict failure rates for electronic equipment based on the reliability data handbook UTE C 80-810 published by UTE (Union Technique de l’Electricite)
  • Consider the effects of phased mission profiles on operating and non-operating components
  • Account for the effects of thermal cycling on the component failure rate due to variations in the ambient temperature and component switch on and off
  • Predict life expectancy for components where applicable

MIL-HDBK-217 arrow icon

With this standard, you can use one of two reliability prediction methods:

  • The parts count method – this simple approach can be used during the early design phase and requires basic information like quality, quantity and environment.
  • The part stress method – this is more complex and requires detailed information on temperature conditions and electrical stress. It can be used when the actual hardware and circuits are being designed.

To build an MIL-HDBK-217 model you define your systems, sub-systems and components in the Prediction tree, then add accurate data to each component in your system or sub-system. RWB Prediction will then calculate your failure rates automatically.


NSWC handbook arrow icon

NSWC Standard 98/LE1 (Handbook of Reliability Prediction Procedures for Mechanical Equipment) is the US Naval Surface Warfare Center standard for the reliability prediction of mechanical components. It uses a series of models for various categories of mechanical components to predict failure rates which are affected by temperature, stresses, flow rates and various other parameters. The categories of mechanical equipment covered include:

  • Seals and gaskets
  • Springs
  • Solenoids
  • Valve assemblies
  • Bearings
  • Gears and splines
  • Actuators
  • Pumps
  • Filters
  • Brakes and clutches
  • Compressors
  • Electric motors
  • Threaded fasteners
  • Mechanical couplings
  • Slider-crank mechanisms

Many of these categories are composed of a collection of sub-components which must be modelled by the user. Typical collections include:

  • Valve assemblies – poppet/sliding action assembly, seals, springs, solenoids, housing
  • Pumps – shafts, seals, bearings, casing, fluid driver
  • Brakes & clutches – actuators, bearings, friction materials, seals, springs
  • Couplings – gears, seals, housing
  • Slider crank – bearings, rods/shafts, seals/gaskets, actuators
  • Electric motors – bearings, motor windings, brushes, armature shaft, housing, gears

In using this standard, familiarize yourself with both the equipment and handbook to ensure the correct type and number of sub-components can be included in the model.


Quanterion 217 Plus arrow icon

The 217 Plus prediction module incorporates component failure rate prediction models developed by Quanterion Solutions, and considers:

  • Quantities
  • Adjustment factors
  • Year of manufacture
  • Duty cycle
  • Cycling rate
  • Ambient temperatures (operational and non-operational)
  • Other part-specific variables

Siemens SN 29500 arrow icon

This is a Siemens AG standard for the reliability prediction of electronic and electromechanical components. The Reliability Workbench SN 29500 module implements all sections (1 through 16) of the Siemens SN 29500 standard:

  • SN 29500-1 Expected values, general (April 2015)
  • SN 29500-2 Expected values for integrated circuits (September 2010)
  • SN 29500-3 Expected values for discrete semiconductors (June 2009)
  • SN 29500-4 Expected values for passive components (March 2004)
  • SN 29500-5 Expected values for electrical connections, connectors and sockets (June 2004)
  • SN 29500-7 Expected values for relays (November 2005)
  • SN 29500-9 Expected values for switches and buttons (November 2005)
  • SN 29500-10 Expected values for signal and pilot lamps (December 2005)
  • SN 29500-11 Expected values for contactors (April 2015)
  • SN 29500-12 Expected values for optical components (February 2008)
  • SN 29500-15 Expected values for electromechanical protection devices in low voltage networks (October 2014)
  • SN 29500-16 Expected values for electromechanical pushbuttons, signalling devices and position switches in low voltage networks (August 2010)

To build a SN 29500 model you define your systems, sub-systems and components in the Prediction tree; and then add accurate data to each component in your system or sub-system. RWB Prediction will then calculate your failure rates automatically.

 


Telcordia SR-332 Issues 2 and 3 arrow icon

With this standard, you can perform reliability predictions using one of these three methods:

  • Method I – based on a parts count procedure
  • Method II – based on combining laboratory test data with parts count data
  • Method III – based on combining field tracking data with parts count data

The Telcordia SR-332 Standard also provides models for predicting the failure rates of units and devices during the first year of operation. The failure rate during this wear-in phase is expressed as a multiplying factor operating on the predicted steady-state failure rate. This First Year Multiplier (FYM) is influenced by burn-in times and temperatures, and is automatically calculated based on specified system, unit and device burn-in times and temperatures.

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