Applying Machine Learning to Maintenance Operations

The future is here. Smart devices know what you need and when. A machine has beat champions at a game thought to be only masterable by humans. Cars are on the verge of completely driving themselves. The list goes on, and it’s about time this happens in industrial settings.  

If you’re a manufacturer with thousands of machines on a factory floor, how do you optimize planned downtime? Do you have an automated way to predict issues with your equipment, or are you relying on reactive maintenance, otherwise known as the “if it ain’t broken, don’t fix it” approach?

Manufacturing in today’s competitive markets requires efficiency and quality production. Unplanned downtime in just one machine can cause delays that can directly affect a company’s bottom line. The good news is that the growth of industrial IoT has given manufacturers an effective way to leverage machine data and machine learning technologies to limit the cost and effects of downtime.

Machine learning is key to improving your existing maintenance program, as it provides higher predictive accuracy. The challenge faced by manufacturers today is that they don’t understand their data. The problem is worse when you apply machine learning without a full understanding of the data and the problems to solve. If the data contains irrelevant information, the machine learning algorithms will not perform well.

Machine Learning Can Help You Get There

But what if you could explore your data to understand what the metrics mean within the context of your assets and their components? For example, knowing that “fuel flow ration” metrics for an engine would indicate the rate of engine fuel flowing through the engine. Knowing your data would also help with understanding how the metrics change and what their typical deviation in values is over time. Back to the engine example, a core temperature metric typically would increase as the engine wears out.

Now what if you could intelligently choose the machine learning techniques based on your available data, the problem you need to solve and your business objectives? By selecting the appropriate techniques for your dataset, you would avoid costly repairs while maximizing the use and availability of the equipment. As a result, you’d catch anomalies in automated operations before they became major challenges that would affect the business.

Leveraging All the Data

Machine learning relies on data to be powerful. The more data you have about your operations, the more machine learning can do to improve the MRO (maintenance, repair, and overhaul). This is where Splunk for Industrial IoT can help. With all your data in Splunk, you can easily leverage Splunk machine learning tools to understand your data and construct machine learning models to start taking predictive action in no time.

If you’d like to dive deeper in how machine learning can directly help you conduct predictive maintenance, try out the Splunk Essentials for Predictive Maintenance app on Splunkbase.





Designed for interactive learning, the app focuses on guiding users through a learning journey on how to solve real problems with real data. You're provided with details of the predictive maintenance methodology and performance data from airplane engine sensors and then asked to operationalize your analytics.

So what are you waiting for? Get started here!

Young Cho

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Applying Machine Learning to Maintenance Operations

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