Improve Your Maintenance Schedule with Predictive Insights

People who know me will agree that I like to tell a story.  I can already reveal that the end of this story is much like a fairy tale.  The beginning, though, is rather boring.  This is a story about a machine.

You can imagine any machine, big or small, with a lot of components and sensors.  This machine also has a controller, a piece of electronics that will check if the components of the machine work like they have to.  The controller will e.g. notify the maintenance technician that he has to check one of the components and maybe replace it.  Rather boring, like I said.

We live in an exciting world, though, a world of artificial intelligence (AI), machine learning and big data.  There’s all sorts of things we can do with the vast amount of data that comes out of the machine.  This is where the story will turn into a fairy tale.

Let’s start with the machine itself.

Machine sensors and sensors that guard the environment of the machine, e.g. to measure the temperature near the machine, or give a visual image of a rotating component, produce tons of data.  Real-time data will give us high-value operational insights.  Real-time alerts to the relevant service engineers enables them to address needs before operational impact.

To give the story the magical touch, we want to look into the future.  We’re not real magicians, but it’s getting close: to simulate the future, we make a digital twin, a virtual software model that replicates how the machine works.

We actually make a digital twin of the machine and another digital twin of the controller.  Without messing with the actual environment, we get a good idea of what might happen when we change a setting in the machine.  Lower the speed of a component to reduce the maintenance, or raise the speed to improve production rates?  The digital twin can give us a predictable outcome.

With the final touch we give our service engineers a tool to compare their own experience with the data from the machine.  A good idea to improve work will be weighed against the available data and predictable outcomes, just by putting the idea in the cloud-based application.

Making good use of the available data will increase the availability of the machine, reduce operating costs, and in some cases bring a higher customer satisfaction.  When you read this story, you imagined some sort of machine, but probably your imagination didn’t go as far as the real world.  The above scenario is actually used in the attractions and shows of Disneyland to deliver the magical experience you expect.

The FACTS: you have read the story, but what would you need to bring this use case into your own environment?

Your scenario doesn’t have to involve an attraction park.  It can be simpler, or more complicated.  Sensors will utilize different electromechanical, industry control and drive systems, so the first thing that you need is an open system for the data integration.

If you want to work with real-time data, the infrastructure has to be able to keep up with the speed.  Best performance, high reliability and low latency are key requirements.  Make sure to have a look at the Hitachi Vantara VSP E-series if you’re looking for a midrange solution with enterprise features.

Intelligence will give you the predictive insights you want.  Probably from a combination of edge and data center software.

 

Peter Verbeeck
Business Development Manager
Peter.Verbeeck@techdata.com

Do you want to learn more about how you can use predictive insights to improve customer satisfaction?

Contact Peter Verbeeck, our Business Development Manager for Hitachi Vantara: hitachivantara@techdata.be

 

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