How does machine learning work with predictive maintenance?

Machine learning allows for more intelligent ways of processing data to predict when an asset will require maintenance. This allows for a more efficient allocation of resources in performing predictive maintenance.

With the emergence of the internet of things (IoT), the ability of everyday objects to collect and transmit data is more easily done than ever. The frequency of collecting data points and the number of assets for which data can be collected, is no longer limited by the human capacity to do so. While predictive maintenance already utilizes available data to predict failure outcomes, machine learning takes this a notch higher by applying algorithms and statistical models to available data – thereby allowing more exhaustive methods of predicting failure scenarios.

Machine learning works by employing several available learning algorithms that interprets historical data to predict future outcomes. One of the most commonly applied learning methods is through the use of regression models – that is taking the graphical representation of historical data to predict future outcomes given similar conditions.

How does machine learning through regression work?
At a high level, machine learning is able to take historical data and identify the parameters that precede certain failure outcomes. Performance and operational data that are continuously being collected by installed sensors can be plotted in graphs over time. For example, given a certain duration of time, the performance of certain equipment can be logged and plotted. Given a plot over time, regression models can be used to predict the factors that can cause failure events that have already previously occurred.

READ  What are barriers to IIoT adoption?

Is machine learning for me?
Knowing how machine learning works and the conditions that make it applicable give you an idea on which parts of your plant would benefit from it more than others. Some key points that can help you assess whether machine learning is applicable are the following:

• Data quality
Do you have the right data to predict specific outcomes? Is you data clean and validated? DO you have enough data points that can be trained to provide useful predictions?

• Machine learning platform
Which machine learning platform is most applicable to your operations?

• Data scientist resource
Do you need a dedicated data scientist that can be integrated into your operations?

• Sharing the data output
Are you able to share and scale the information from one asset across the whole plant?