# Engineering Data

In Data Visualisation I looked at the general concept of using a Visualisation Method to make the meaning of data more apparent and gave two examples. In this post I look at how we use this is our own business creating new Electronics Products.

Engineering Data is the information used by Engineers to do their work. In our case the Engineering Work is Electronics Design. Data Visualisation is used at both the Engineering Design phase of a project and also the Test and Verification phase. For this post we will look at how test results can be better understood visually.

# 3D Gravitic Sensor

The first example is a 3D gravitic sensor used for Solar Tracking. We were required to keep the panels on sun using an almanac and had to maintain this within 0.5° because this was a concentrated solar system. The cradle angle was not simple to measure because the panels are on an angle to suit the latitude of the installation. But we had to now the cradle angle within 0.25° to be sure we were on sun within 0.5°. This was our share of the error budget, another Engineering Concept. Here are the results in tabular and graphical form.

First the table:

 Digital Protractor Angle Gravitic Angle Gravitic Error -90 -90.10 -0.09 -80 -79.98 0.01 -70 -70.12 -0.12 -60 -60.17 -0.17 -50 -49.97 0.03 -40 -39.90 0.10 -30 -30.08 -0.07 -20 -20.02 -0.01 -10 -9.89 0.10 0 -0.09 -0.09 10 9.96 -0.03 20 19.94 -0.05 30 30.04 0.03 40 39.98 -0.02 50 50.10 0.10 60 60.07 0.07 70 70.01 0.01 80 80.17 0.17 90 90.22 0.21

Then the graph:

Gravitic Error Versus Angle

In this case the graph makes it immediately apparent that the unit passes the test. It would have been even easier to see if there were solid red lines at +0.25° and -0.25°.

# Precision Temperature Measurement

The second example is a set of PT1000 RTDs used for precision temperature measurement. For this project the required accuracy was 0.5°C absolute and 0.2°C relative to each probe. A simple test was done where we put the probes into recently boiled water and recorded the temperature using a precision temperature data logger we had developed . We knew that the near step change would cause initial divergence in the results but we wanted to see how quickly they settled. So the options were three columns of data points 16,000 readings high, or graph it. Guess which was easier to understand!

Here is the end of the numerical results with the initial 15,992 rows not shown:

 Time RTD1 RTD2 RTD3 … … … … 11/07/2011 15:17 21.6 21.7 21.7 11/07/2011 15:17 21.5 21.7 21.6 11/07/2011 15:18 21.5 21.6 21.6 11/07/2011 15:18 21.5 21.6 21.6 11/07/2011 15:19 21.5 21.6 21.6 11/07/2011 15:19 21.4 21.5 21.5 11/07/2011 15:20 21.4 21.5 21.5 11/07/2011 15:20 21.4 21.5 21.5

And this is the graph of all the RTD temperature readings:

Temperature Graph

And finally, this is a graph of just the differentials between RTDs:

Temperature Differential Graph

This last graph makes it much easier to see that the probes settle to within 0.2°C of each other almost immediately and stay there or below for the rest of the graph.

The three sets of results are the same data. But how we look at it changes how easily we can understand it.

There are many other examples possible but this is enough to show the idea in action.

Successful Endeavours specialise in Electronics Design and Embedded Software Development. Ray Keefe has developed market leading electronics products in Australia for nearly 30 years. This post is Copyright © 2012 Successful Endeavours Pty Ltd

# Seeing Information

My previous post on Information Overload identified the problem we have with handling all the data that is being created in our modern Information Age world. This post has 2 simple examples of the power of information and how it can be more easily understood using Data Visualisation techniques.

I recently received an email showing the expected growth trends in the Australian economy in 2012. One of the things I found hard to make sense of from the article was exactly which sector was doing better so I decided to pull the information from the post for the sectors that had it, put it into excel and do a graph. This is the essence of Data Visualisation. Here is what I came up with.

Data Visualisation

So from this we can now see, that is the point, that the Resources and Energy sector is expected to grow very strongly whereas Advertising and Marketing is shrinking. I sent a copy of the graph to our business mentor Dr Marc Dussault, The Exponential Growth Strategist, as I thought he would be interested. He reworked it slightly and sent back this:

Better Data Visualisation

There are 2 changes here. The first is that the data is ordered so the trend is clearer. Organising the data better can improve the understanding you get from the same data using the same Visualisation Method.

The second change is that the formatting of the data is more attractive which makes it more likely the information will be looked at. Marc says that is because he did his graph using a Macintosh computer whereas I used a PC running Windows. That is a long running debate but in this case the visual results are clearly better. So formatting the visualisation also helps.

# Seeing Connections

Sometimes it is the relationship between pieces of information that is important. This second example is our website. This is done using a HTML Tag Diagram Viewer. My thanks again go to Dr Marc Dussault, The Exponential Growth Strategist for providing this link. The link redraws our primary domain but you can just put your own in if you want to see what that looks like. It was created by Marcel Salathe so my thanks also go to him for creating and making freely available such a useful tool.

Here is what the graph for http://www.successful.com.au looks like.

www.successful.com.au Website Visualisation

This shows how the tags on the pages relate to each other and how the pages link from the home page in the centre to the rest of the pages on the domain. The blog shows as a cluster only in this view. Here is the legend for understanding the colours.

• Blue: for links (the A tag)
• Red: for tables (TABLE, TR and TD tags)
• Green: for the DIV tag
• Violet: for images (the IMG tag)
• Yellow: for forms (FORM, INPUT, TEXTAREA, SELECT and OPTION tags)
• Orange: for linebreaks and blockquotes (BR, P, and BLOCKQUOTE tags)
• Black: the HTML tag, the root node
• Gray: all other tags

So this information is both graphically represented and also Colour Coded, another Data Visualisation technique.

I then decided to see what just this blog would look like.

www.successful.com.au/blog Website Visualisation

So the blog is a lot more complicated. And that also makes sense. There are more outgoing links and more interlinking since I also reference other posts.

As an Electronics Design company we use Data Visualisation all the time to help us analyse both research results and test results. So I plan to show a few examples of that in my next post.

Successful Endeavours specialise in Electronics Design and Embedded Software Development. Ray Keefe has developed market leading electronics products in Australia for nearly 30 years. This post is Copyright © 2012 Successful Endeavours Pty Ltd