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Understanding Predictive Maintenance — Data Acquisition and Signal Denoising
Article purpose
I want to start a series of articles to give you hands-on experience in predictive maintenance and make it easy for you to get into signal processing. In this article, we’ll concentrate on getting data and cleaning up the signals. If you find certain parts interesting, I’ll think about going into more detail. In the next part of this article, I’ve got some practical exercises for you. You can use the code I’ve prepared to do your own experiments and learn by doing.
Data science of predictive maintanance
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 fancy computer programs and past data to predict when something might go wrong. It’s a bit like having a crystal ball for machines! By doing this, companies can save money and keep their important machines working longer. This method involves keeping a close eye on machines, collecting data in real-time, and using clever computer programs to tell us when it’s time to do 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 health check before they get sick!
