Machine learning, artificial intelligence, the industrial internet of things—what do each of these have in common? These are buzzwords you probably have been hearing lately. But what do these words mean? Let’s break down what machine learning actually is.
What is Machine Learning?
First and foremost, let’s talk about what machine learning is not.
For example, consider advanced condition monitoring using sensors. Sensors can be placed all around a machine, and algorithms can be set to warn you of something based on a certain condition occurring. This is not machine learning. While this practice does involve advanced technological tools, machine learning is more sophisticated. Machine learning is an application that provides the ability to automatically “learn” from experience, without being explicitly programmed.
Machine learning fundamentally encompasses the capability of advanced software to recognize patterns, and then begins to learn from those patterns—meaning you are not manually programming the algorithm, telling the software what to look for in order to trigger an appropriate response.
With machine learning, the software is able to look at all kinds of data and compare existing conditions to past conditions—the software learns how to recognize patterns and learns that certain patterns mean certain things. It learns when things are normal, verses when they are abnormal.
These capabilities mean that, as time goes by and various things are experienced, machine learning software will analyze parameters to recognize and predict events in the future.
How can machine learning be applied in the industrial setting?
Based on the discussion above, machine learning can be a game changer when it comes to managing the reliability of your assets.
For instance, by looking at process parameters, machine learning technology can recognize patterns leading up to any number events—whether it be a failure, a level, a temperature, a pressure, etc. It recognizes that an undesirable event is approaching and then it gives you warning of said event. And if the warning is far enough in advance, you often can prepare for and even prevent the undesirable event from happening.
In contrast, older technology (predictive maintenance, condition monitoring) provide some advance warning of asset failure. However, with these technologies, the asset has likely already entered the failure scenario. Which means damage is already occurring in your equipment before the warning is triggered.
With advanced machine learning software capabilities, we can now use technology to evaluate process parameters way upstream and downstream of the equipment to understand and recognize the patterns that lead up to a failure. Thus, machine learning provides the capability to potentially be warned of a damage mechanism before it occurs, which enables you to actually prevent the failure from happening.
Machine Learning: Next Steps
Are you interested in hearing more about machine learning’s application in industry? Stay tuned for future content in which we will cover the following topics:
- Are you ready for machine learning?
- What to do if you don’t have historical data?
- Will machine learning change the way people do their jobs?
- What actions should you take if you are ready to consider implementing machine learning?