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.
I promise.
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.
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.
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.
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 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 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.
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 26th.
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 video description as well.
I hope this video helped you get a basic understanding of
how machine learning can be used for predictive maintenance.
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