The Importance of the Context in Data Collection

 The Importance of the Context in Data Collection 

  The Importance of Context in Data Collection

By George Walker, Managing Director of Novotek UK and Ireland

It is no secret that data collection is important in gaining valuable insights that help asset managers improve their business performance Improve efficiency in industrial systems. What seems unknown to many in the industry, however, is the importance of understanding the data to be analyzed. This article explains why applying context to your data is the difference between insight and statistical blindness.

Tunnel vision is a problem in a variety of areas, from sports to industry. It's easy to focus on a single goal and forget to step back to get a broader view of a situation. But if you step back, you can get an invaluable insight into the reality of a situation.

Let's take a hypothetical scenario. Imagine, a maintenance manager has a machine that bends wires. These wires must bend at a very precise angle to work properly. However, the machine does not properly turn it at certain times of the day, so the company produces faulty goods that need to be disposed of.

To fix this problem, the maintenance manager calls software for the data collected from the device on board. The manager then analyzes the data with a digital twinning platform. The analysis shows that the machine vibrates abnormally at certain times of the day. The manager disassembles the machine, rejoins it, and even executes it in an isolated scenario, but still can not find the cause of the problem.

In this case, the manager would have needed a moment to step back and look up. In context, they may have realized that the anomalous vibrations coincided with the activation period of a nearby heavy machinery. There is nothing wrong with the machine in question, but the surrounding context reveals the cause of the error. That's why the context of data collection is crucial.

Using a range of smart sensors and devices, coupled with a digital twinning system such as GE Digital's Predix platform, the manager could clearly see the correlation through viewing the data in context. Another method to achieve a contextual understanding would have been to compare the machine to other similar systems in different systems.

This shows why digital twinning is such a powerful tool. The ability to replicate an entire system in a digital model breaks the silo mentality. This gives managers a holistic insight that reveals issues that were not previously obvious. Whatever the problem, it is obvious that the context is important in data collection. Being able to analyze systems is now a reality and should be fully exploited.

With the potential to reduce waste and unnecessary spending, digital twins will enable greater operations. Because the context is easily accessible in modern industry, it no longer has to be a trade secret for industrial companies. By sharing this knowledge, more companies can make their smart networks smarter, streamline their operations, and make their production processes more productive.

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2018-11-26 20:25:45
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