GradientOne provides several tools for performing meta-analyses of results. As devices become increasingly complex, the point or points of failure can be harder to identify. Incorporating data mining techniques such as machine learning or exploration into the process of brining new products to market can help at multiple stages in the pipeline. It can help in the research and development phase by predicting the possible avenues of implementation most likely to be fruitful. It can help in the manufacturing stage by identifying faulty equipment before the final steps in the process, thus saving operation costs and time, and it can help in the market stage by identifying devices that need to be recalled or patched before failures are reported. This first post will go over how to visualize and explore numerical data with this hypothetical example.
A manufacturer of LED TVs wants to investigate the causes of pixel failure in their TVs. If there are no dead pixels in a screen, the screen is tagged as a Pass. If a single pixel on a screen is dead, it is tagged as a Warning, because most people won't notice a single dead pixel. If more than one pixel on the screen is dead, it is tagged as a Fail.
During the testing phase, the gain (meaning how much current is produced for the incident light intensity), current, and the temperature for each pixel is measured. These results were generated by running a configuration called "pixel". The average gain, average current, average temperature and max temperature are stored in the metadata section of each result:
Since this data is numerical, the first thing a test engineer can do to investigate the cause of failures is to look at the scatterplots. On the Analysis page, the test engineer would click the checkbox next to Scatterplot Matrix, under Meta Analysis Suites and then on Run Selected
On the modal, enter pixel in the Select Data box, and wait for the modal to show showing results for "pixel". Then click on Select All
Then scroll all the way down the modal to the run button:
The gears will turn as the metadata is compiled, and a link to the meta result will appear after the command is complete:
The View Results link will take you to the scatterplot. The factors that appear on the x and y axis can be changed using the drop-downs to the right of the chart:
By selecting the gain and current values as axes, we can see a cluster of passes around the high-gain low-current corner of the plot, but no clear cluster distinction between the warnings and failures:
However, when the axes are changed to average an max temperature, all three clusters become apparent:
From this, the test engineer can conclude that although there are correlations between gain, current, and passing screens, the real cause is likely to be the temperature.