Once the data are acquired they can be quickly validated to prevent errors slipping into the system – and all before the setup has been broken down.
There’s nothing more expensive than wrong or incomplete data–but few test drivers have the time to be data acquisition specialists. Our InField product can enable a driver with limited measurement experience to quickly preview and verify data at the trackside. But ICE-flow itself provides more…
The powerful ICE-flow GlyphWorks data validation and ranking tools can also run at the trackside, and provide an automated and in-depth analysis of the raw data–no matter what measurement hardware was actually used. The validation procedures are defined “back at base” and uploaded to the field–this ensures consistent and approved procedures are applied. Naturally, advanced users can have more interactive access to the full system capabilities. Comprehensive procedures are provided, such as an automated anomaly detection sequence–which not only identifies the more obvious problems such as drift, dropout, spikes, overloads–but also abnormal variances between channels and several other subtle test errors that can only be detected statistically. Many of these can be corrected automatically.
More on ICE-flow GlyphWorks and GlyphWorks Anomaly Detection
With ICE-flow, the team back at base can see what is happening at any time–they do not have to rely upon the driver to run the validation routines. Data interrogation via satellite links mean that raw data can be uploaded then validated shortly after the test ends. From anywhere in the world, for example, the driver could be advised to check a suspect transducer connection, or the test could be modified to include a higher sampling frequency.
Typical tests involve thousands of long time histories, so ICE-flow TestManager has a graphical way of showing the current status of the data. As soon as channels of data are available each indicator changes from white (no data) to black. As validation and cleaning proceeds, the indicator light turns green when new data is “good,” yellow when a problem has been discovered (but the data can be used), or red when the data is “bad” and not recoverable or useable.
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