Putting the AI into Maintenance.

Position:DATABASE AND NETWORK INTELLIGENCE: NEW RESEARCH
 
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Machine learning is streamlining predictive maintenance. Sean Robinson, Novotek.

Over the past five years, the industrial sector has begun to see the value in digitalisation and has invested more in adopting it. With this has come a cultural shift from reactive equipment maintenance to proactive maintenance that pre-empts problems. Here, Sean Robinson, service leader at industrial analytics platform supplier Novotek UK & Ireland, explains how plant managers can make proactive maintenance even more effective with machine learning.

In 2006, UK mathematician Clive Humby claimed that "data is the new oil". Whether you're a food processing company or an automotive manufacturer, data from production processes is the cornerstone of better efficiency, effectiveness and overall performance.

Plant managers that are familiar with the industrial internet of things (IIoT) will know that one of the concept's biggest selling points has been the insight it can provide into equipment performance and process effectiveness, which in turn creates benefits for the company's bottom-line.

This has changed the culture of maintenance in plants that have started adopting lIoT technology. Rather than responding to a breakage or conducting planned maintenance based on expected equipment lifespan, engineers can make informed decisions about when to maintain systems based on the equipment's condition.

Minimising unplanned downtime has obvious benefits, but it's the reduction in scheduled downtime that adds significant value in terms of increased overall throughput for no new capital outlay. However, achieving this is challenging due to the volume of data and subsequent analysis that is required to confidently change maintenance schedules.

This is where an opportunity arises for machine learning in industrial maintenance.

With machine learning, algorithms can be trained to identify correlating factors in data to not only flag up a problem but also the root cause of it. It sounds straightforward in principle, but the number of potential things to consider can be too high for a human to work through effectively. Within a single machine, there can be dozens of sensors or other health signals. To get a clear picture of all the things that affect reliability, that data should be evaluated alongside things like maintenance records and a history of what the machine was running. Even ambient conditions and crew data can give clues as to...

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