## How to Use Machine Learning for Predictive Maintenance

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⌚Timestamps:
00:00 – Intro
00:14 – Motor vibration example
00:47 – How do we know when the vibration is unusual?
01:54 – Normal operating condition
04:28 – Webinar Registration

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Here 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.

We have two vibration sensors here. 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 there is something wrong with the motor, we’ll have an unusual vibration.

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.

To do this, I can simply measure the vibration for sensor A, at a random point in time, and write it down. Next, and at the same time, I can measure the vibration for sensor B. So at one point in time, I measured the vibration for both sensors.

I will repeat this measurement one more time. 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.

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 8, and at the same time, the value of 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 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 there might be something wrong with the motor.

This is how I can use machine learning to detect the unusual behavior of a machine.
So this was a simple example of using machine learning for predictive maintenance.

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