Data Curry

Understanding machinesUnderstanding machines

These stories are written by Dr. Dakshinamurthy V Kolluru, Chief Advisor – Data Science,

Usha Martin Education: President, International School of Engineering (http://www.insofe.edu.in)

The best place in the world to learn Applied Engineering.

We have been hearing “internet of things” a lot recently. Modern day machines have sensors everywhere, they are connected to

the cloud and we are collecting a lot of data from them.

So, what can we do with the terabytes of data we collect?

Recently, a company asked me to extract any hidden patterns of failures from this extremely expensive machine. They gave me

3 years of data on a minute-by-minute basis.

I tried correlating every possible variable with failure; I studied the failure time in a day as a function of the average operational

parameters, process parameters, raw materials, vendors, climate, seasonality, etc.

I could not find any trend, however hard I searched.

Finally, I decided to look at the behavior every hour. The following plot shows the duration of failure during various time slots of

a day.

The X-axis has six time slots and the Y axis is downtime

in minutes. Each dot represents average downtime

during the corresponding shift taken on daily data

over three years.

Clearly, there is a dip in downtime in 3 and 4. Once we

found it, the business could explain it. They had shifts

every 8 hours. The downtime is least in the 8 AM shift

and highest in the night shifts.

They concluded (rightly) that the agility of the

operators (which is high in the morning shift) was

leading to minimization of faults.

So, with the big data capabilities, you can learn a lot

from logs and signals.

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