How to Use Machine Learning for Predictive Maintenance

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.

Let’s start with a simple example. Let’s say that we have two vibration sensors. Vibration sensors are usually used on rotating equipment such as motors, fans, pumps, gearboxes, and so on.

Let’s say that 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 there 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 here 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 2.

Next, and at the same time, I can measure the vibration for sensor B. Let’s assume that the vibration for sensor B, is 3.

So at one point in time, I measured the vibration for both sensors. The value for sensor A was 2 and the value for sensor B was 3. Unites of measurement for vibration is not important here. 😉

I will repeat this measurement one more time. This time I see that the value for sensor A is 3 and the measured value of sensor B is 5. As you can see, the values are a bit higher but still kind of in the same range.

One thing to take into consideration here 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 here 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.

Normal operating mode

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.

Abnormal mode

Now, let’s say that one day, and again at a random point in time, I see that the value of sensor A is 8, and at the same time, the value for sensor B is 2. 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 the yellow area, right?

In the image, the yellow area is a 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 there 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.

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