Predictive Maintenance

Predictive Maintenance

Using machine learning to help see the future, to avoid costly unplanned downtime

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Transforming your operations

Working out the right time to carry out maintenance is a delicate balancing act. You want to avoid unplanned downtime, to sustain throughput predictably. You want to minimise planned downtime, so you maximise capacity. All the while, you want to avoid excessive maintenance or parts replacement, and the costs associated with them.

Reduce unplanned downtime

Prevent unplanned downtime and take prompt measure to increase quality with the help of actionable insights from predictive analytic.

Increase quality

By analysing your machine data from multiple sources, you can discover production faults and improve product quality.

Improve yield

With the the power of real-time insights and analytics you can optimise processes, save operational costs and increase yield.

Our Methodology

At Astec, we have a proven and sound methodology for implementing predictive maintenance successfully. We also work with the data scientists because we understand the depth of experience required to deliver effective machine learning solutions in manufacturing.

The projects follows a three step process as outlined below:

Predictive Maintenance Process

Powerful machine learning

Machine Learning

The beauty of machine learning is that the system can work out for itself which markers are relevant. You provide a range of historical data, and information about when the machines failed, and the solution can find the correlations automatically, including some that might be difficult for a human to spot.


While machine learning can derive surprising insights from diverse data sources, too much data might dilute the correlations or cause false positives, so it requires skill to select the right data. Our data scientists  identify the relevant data to feed into the machine learning solution, validate it, and clean it so it’s ready for processing.

Astec’s machine learning experts train the solution using a subset of the cleansed data. Once it’s been trained, we then validate it with the remaining cleansed data to confirm that it can predict the failures accurately.

When it is ready, it can be connected to the live data sources. As the solution is working, it continues to learn, so it gets better and better.

Find out more

To find out more about how machine learning can help your manufacturing business contact us.

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