How to Use Machine Learning for Predictive Maintenance

well the first question is what is machine learning well machine learning is a sub-domain of computer science that focuses on certain algorithms which might help a computer learn from data without a programmer being ton this article telling the computer exactly what to do that’s what we call explicit programming so you might have heard of ai and ml and data science what is the difference between all of these so ai is artificial intelligence and that’s an area of computer science won this article the goal is to enable computers and machines to perform human-like tasks and simulate human behavior now machine learning is a subset of ai that tries to solve one specific problem and make predictions using certain data and data science is a field that attempts to find patterns and draw insights from data and that might mean we’re using machine learning so all of these fields kind of overlap and all of them might use machine learning .

On this article is a basic example of how machine learning can be used for predictive maintenance. You don’t need to be an engineer to understand this. It’s very basic and fun and you can understand it very easily. I promise. We have two vibration sensors on this article. Vibration sensors are usually used on rotating equipment such as motors, fans, pumps, gearboxes, and so on. Let’s say we have our two vibration sensors installed on an electric motor. We can use these sensors to measure the vibration of this motor. In normal operation, we have normal vibration for the motor.

But, when ton this article is something wrong with the motor, we’ll have an unusual vibration. Very basic, right? Now, the question is, how do we know when the vibration is unusual? What should we consider an unusual vibration? Well, to do this, these days with all the advancements in Al or Artificial Intelligence, we can use simple machine learning techniques to figure this out. But don’t let terms such as AI and machine learning scare you. The concept is fairly basic! Let me show you how. Again, what I’m trying to do on this article is to use a simple machine learning technique to determine what should be considered an unusual vibration. To do this, I can simply measure the vibration for sensor A, at a random point in time, and write it down. Let’s say that the measured vibration for sensor A at this point is . Next, and at the same time, I can measure the vibration for sensor B. Let’s assume that the vibration for sensor B, is . So at one point in time, I measured the vibration for both sensors. The value for sensor A was and the value for sensor B was . I will repeat this measurement one more time. This time I see that the value for sensor A is and the measured value of sensor B is. As you can see, the values are a bit higher but still kind of in the same range. One thing to take into consideration on this article is that it does not matter when I measure these values. I mean I could measure these values at any random point in time. But the only thing that matters in this article is that the measurement for both sensors should happen at the same time. That means I need to measure the values for both sensor A and sensor B at the same time. Ok, I will repeat this process for measuring the values for both sensors a few more times. By doing this I can get more data points about the vibration of the motor in normal conditions. Now, why do I measure these values or data points, you ask? The reason that I measure these values is to be able to come up with some sort of model for the motor vibration in normal operating conditions. That means, using these data points, I can now have a pretty good understanding of what the motor vibration value could be approximately when the motor is operating in normal mode and without any problems. Now, let’s say that one day, and again at a random point in time, I see that the value of sensor A is , and at the same time, the value for sensor B is . This is clearly an unusual value. How do I say this? Because I’ve already measured the vibration of this motor several times and I have LEARNED that when the motor is operating in normal mode and without any problem, the vibration values should usually fall in this area, right? This is the model that I have developed for the times that the motor is operating in normal mode and without any problem. Now that I see a value outside of this area or outside of this model, I can easily say that this new value is not normal and can indicate that ton this article might be something wrong with the motor. This is how I can use machine learning to detect the unusual behavior of a machine. Meaning that using simple machine learning techniques I can create a simple model of normal operating conditions for any machine or application and determine the values that fall outside of that normal area.

 So this was a simple example of using machine learning for predictive maintenance. If you want to learn more about predictive maintenance make sure to sign up for RealPars first-ever live training event on Oct th. For this live training session, we have teamed up with Edge Impulse to teach you how machine learning can help with predictive maintenance for industrial applications. To reserve your spot, simply head on over to this page and enter your name and email address to get all the details. We’ll add a link to the article description as well. I hope this article helped you get a basic understanding of how machine learning can be used for predictive maintenance. If you found this article helpful, please give it a thumbs up to make it easier for other people to find and watch this article.