
Reliability prediction, allocation and derating analysis
ReliaSoft Lambda Predict facilitates reliability prediction analysis based on the major published standards, including MIL-HDBK-217F (MIL-217), Bellcore/Telcordia, FIDES, NSWC Mechanical and Siemens SN 29500. It offers a full set of supporting tools, including easy-to-use component library functionality, a reliability allocation utility, derating analysis and the ability to transfer and manage your data via flexible import/export or copy/paste with complete array of calculated results, graphical charts and customizable reports.
In addition, Lambda Predict provides exclusive access to the PartLibraries.org website portal, which allows users to search and import parts data from MIL-M-38510, EPRD-97 or NPRD-95 (free to all users) as well as more than 300,000 specific commercial electronic components.

Discover key capabilities that make Lambda Predict an essential tool for performing reliability predictions
Evaluate design feasibility
Obtain an initial indication of whether a design will be able to meet reliability objectives and identify potential problem areas early in development.
Identify potential reliability areas of concern
Lambda Predict enable you to perform an assessment of likely failure rate characteristics. By predicting failure rates, you can make design changes as needed for areas of weakness.
Compare designs to make the best decisions
With Lambda Predict you have an ability to asses and compare design alternatives and/or trade-off system design factors.
Track reliability improvement
Consider environmental and other stress factors that have a significant impact on system performance yet may otherwise be overlooked.
Meet compliance requirements with reliability based standards
Lambda Predict supports all major reliability prediction standards: MIL-HDBK-217F, Bellcore/Telcordia, FIDES, NSWC Mechanical, and Siemens SN 29500 standards.
Gain free access to build-in database of component libraries
With Lambda Predict you have an access to PartLibraries.org, where you can search by category, part number, supplier, part name and/or part description.

Component Libraries for Standards Based Reliability Prediction and Other Analyses
Key Features
System reliability, availability, and maintainability analysis
- Build the system configuration (“from scratch” or by importing data from “Bill of Materials” files, predefined part libraries or other outside sources)
- Define component characteristics and operating conditions, with the choice of two different “Views” to facilitate data entry
- Calculate results (Pi Factors, Failure Rates, MTBFs, etc.) at any level within the configuration
- Use graphical plots/charts to visualize and present analysis results
- Generate print-ready reports to support decision-making and disseminate knowledge

- NAVSEA-TE000-AB-GTP-010 issued by the Naval Sea Systems Command in March 1991
- MIL-STD-975M (NASA) issued by the US National Aeronautics and Space Administration in 1994
- MIL-STD-1547 issued by the US Department of Defense in November 1998
- Naval Air System Command AS-4613 issued by the US Naval Air Systems Command in 1976
- ECSS-Q-30-11-A issued by the European Cooperation for Space Standardization in April 2006
- Equal allocation
- AGREE allocation
- Feasibility of objectives allocation
- ARINC apportionment technique
- Repairable systems allocation
- Support for both part stress and parts count analyses
- Ability to define custom connections
- Non-operational failure rates
- SR-332 Issue 3 (Telcordia Technologies, 2011)
- SR-332 Issue 2 (Telcordia Technologies, 2006)
- SR-332 Issue 1 (Telcordia Technologies, 2001)
- TR-332 Issue 6 (Bell Communications Research, 1997)
Failure rates, MTBFs and other calculated results are displayed directly in the system hierarchy. You can choose which columns will be displayed at any given time, and also use the Highlights feature to use red, yellow and green background colors to make it easy to see how the component’s failure rate contributes to the overall failure calculated for the system or assembly. With Pi Factors View you can see how the component properties contribute to the pi factors in the failure rate model and use the Calculation Information window to see a report of the pi factors and failure rate calculation for any selected component.
Lambda Predict provides a complete array of plots and charts to demonstrate your analysis graphically. This includes plots for Failure Rate, MTBF, Mission Time and Unreliability. Plot Setup gives you full control over the display settings and all plot graphics can be saved as *.jpg, *.gif, *.png or *.wmf files and inserted into other reports and presentations. The Saved Plots feature provides the ability to save the plot with the project.
For maximum flexibility, Lambda Predict offers template-based reports to present and disseminate the results of your predictions. A basic report template is provided for each reliability prediction standard that is included with your license. You can customize the appearance of these predefined report templates, manage and build your own custom report templates. Reports can be generated in Microsoft Word® and/or Excel®.
Training Courses
Find training for life data analysis, accelerated life testing, and reliability growth with guided usage of Weibull++ software.
Events
Case Studies
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Asset Performance Management Supported by Reliability Engineering
Companies need to understand the environmental impacts of their businesses and to respond to the increasing market pressure driven by investors, consumers, the media, regulators and other stakeholders.

Creating Initial Scheduled Maintenance Plans for Aircraft, MSG-3
Companies need to understand the environmental impacts of their businesses and to respond to the increasing market pressure driven by investors, consumers, the media, regulators and other stakeholders.

MIL-217, Bellcore/Telcordia and Other Reliability Prediction Methods for Electronic Products
Companies need to understand the environmental impacts of their businesses and to respond to the increasing market pressure driven by investors, consumers, the media, regulators and other stakeholders.
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