I want to start a series of articles to give you hands-on experience in predictive maintenance and make it easier for you to get into signal processing. In this article, we will focus on obtaining data and cleaning signals. If you find some parts interesting, I’ll think about going into more detail. In the next part of this article, I have some practical exercises for you. You can use the code I have prepared to do your own experiments and learn by doing.
Predictive maintenance in data science is like having a super smart way of taking care of machines. Instead of fixing things after they break, we use sophisticated computer programs and past data to predict when something might go wrong. It’s like having a crystal ball for machines! By doing this, businesses can save money and keep their important machines running longer. This method involves keeping a close eye on machines, collecting real-time data, and using smart computer programs to tell us when it’s time for maintenance. So instead of waiting for something to break, we can fix it before it causes a big problem. It’s like giving machines a checkup before they get sick!
Everything starts from the data. We need to dig a little deeper into the principles of communication theory, such as the Shannon-Hartley theorem and the Nyquist rate, to ensure accurate and efficient transmission of sensor data.
The Shannon-Hartley theorem is like a rule book for how much information can be sent over a communication channel without breaking down. It tells us that the width of the channel, or the amount of data it can handle, is very important. So before choosing devices or tools to monitor things like machines or sensors, we need to make sure that the channel is wide enough to handle all the data we want without losing quality.
