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Full scale accelerated fatigue test on a rail car

Fatigue testing to predict the life of a railcar requires a previous structural analysis, instrumentation and an Over the Road (OTR) test to collect all the necessary information needed for the generation of the signals used to drive the fatigue test rig (shaker). In some instances, the railcar to be placed on the shaker is not the same car used to develop the fatigue input data and therefore requires an alternate approach.

This presentation describes how real-time, large data inputs from a previous analysis and testing on a railcar is converted into valid, equivalent signals which will be used to drive the shaker while creating the equivalent damage on a different unit of similar design. This alternate procedure typically allows the simulation of 30-50 years of service in a 3-4 months accelerated test.

Value

Reduce test time and cost by eliminating the need for another rail test (4-6 months time-saving typical).
Efficiently test any number of similar railcar designs.

Challenge

Need to develop an accelerated fatigue test on a rail car that is a different design than the rail car that provided all the customer usage data.

Solution

An alternate procedure based on fatigue damage analysis in nCode GlyphWorks.
Generate a metric called Road Environment Percent Occurrence Spectra (REPOS).
These REPOS data allows creation of a test that replicates a long service life in a condensed period of time.

Test data processing and durability analysis

nCode GlyphWorks is a data processing system that contains a powerful set of pre-defined tools for performing durability analysis and other insightful tasks such as digital signal processing. Designed to handle huge amounts of data, GlyphWorks provides a graphical, process-oriented environment that contains leading analysis capabilities for saving both time and money in environmental qualification and product validation.

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Data storage and automated reporting

nCode Automation is a web-based environment for the automated storage, analysis and reporting of engineering data. It is built on robust technology including Java EE to provide a collaborative platform for sharing test data and associated information throughout an organization.

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