Predictive maintenance - creating and implementing production model

In the last two articles about AI-based predictive maintenance, we explained the process and the difference between other maintenance methods and showed two phases necessary to develop predictive maintenance models. In this article, we focus on the final phases, so let’s dive in!

Let’s sum up all the steps necessary to create a prediction model:

Collecting, filtrating, analyzing, and sorting data – machine learning requires large amounts of quality data to be trained on.
Choosing the correct model based on the prediction requirements.
Training, evaluation and hyperparameter tuning – those steps need to be repeated until accurate prediction is achieved.

Lets see what’s going on with the 3rd phase!

Phase 3: Building, installing and implementing the production solution

When we are satisfied with the results of our neural network it’s time to implement the prediction model for the production. We need to design the architecture and develop the prediction application.

Then we need to install and test the solution in the testing environment. Using the most recent production data, we perform both predictive maintenance in the test environment and scheduled maintenance to compare its results with the prediction. This Validation period will confirm the accuracy of the model and allow its implementation on the production enviroment

Once we install and implement our solution, the service can be scheduled less often – the higher the model accuracy, the fewer services are scheduled

After the predictive maintenance solution is implemented on the production enviroment, we still need to gather data. Additionally, we must monitor any information about changes in equipment or its configuration -as this might have an impact on the prediction accuracy. The last phase of that process is production enviroment model maintenance.

Phase 4: Production model maintenance

With the new production data coming in, our model in the testing environment is periodically re-trained and tested against the current model. If its precision is higher, we replace it in the production improving the solution’s accuracy. Older production data become outdated after a time, meaning that we must train the model on more recent data.

Seemingly small changes in the production system can change the prediction accuracy of the model. That’s why it is important to update the production model after modifications of the system.

Benefits

This process may seem very time-consuming but it’s worth it. The effort will return the investment with quite the margin! How can a company benefit from implementing a predictive maintenance solution?

12

Reduced maintenance cost

9

Reduced machine shutdown time for servicing

Reduced risk of safety, health, and quality failures

20

Extended lifetime of an aging asset

Source: Study conducted by PwC.

AI-based Predictive Maintenance is one of many practical applications of machine learning in the industry. Shortly we will break down more of them in the upcoming articles!

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