NASA IoT - Different Ways to Model Predictive Maintenance and Engine Degradation
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- Опубликовано: 22 авг 2024
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Let's go over another great dataset. This one is from NASA and covers IoT-predictive maintenance. They recorded/simulated Turbofan engine degradation. We'll prepare the data and run it through the Fastml.io automl engine (same engine we used in previous classes).
The link to the data is included in the source and so is the data prep code. It contains over 20-sensor measurements and some operational settings and represents a series of prop engine runs - going from optimally running to dead-in-the-water.
For source code:
www.viralml.co...
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Loved your video.. Want more on Predictive Maintenance :)
Thank you!
Unless I'm missing something, this analysis is incorrect. How could Cycle be used as input feature and at the same time the target/output is derived from Cycle? That's why Cycle is the most important feature ...
awesome
Thank you for this model. I have learned a lot. Still one question: Am I right to understand that by using FastML.io, we determined which measurements (dependents) could best be linearly modeled against the Target(RUL) and chose those variables to reduce the scope by which we performed classification?
I guess Im asking the wrong place but does anybody know of a trick to log back into an instagram account??
I was stupid forgot the account password. I love any assistance you can give me.
@Thomas Russell instablaster :)
@Pablo Jamie thanks so much for your reply. I got to the site on google and I'm trying it out atm.
Looks like it's gonna take quite some time so I will reply here later when my account password hopefully is recovered.
@Pablo Jamie it did the trick and I now got access to my account again. I'm so happy!
Thanks so much you saved my ass!
@Thomas Russell Glad I could help =)
wow....