Estimating Remaining Useful Life (RUL) | Predictive Maintenance
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- Опубликовано: 22 авг 2024
- Predictive maintenance lets you estimate the remaining useful life (RUL) of your machine. RUL prediction gives you insights about when your machine will fail so you can schedule maintenance in advance.
You’ll learn about the most common RUL estimator models: similarity, survival, and degradation. You can use similarity models to estimate RUL when you have complete histories from similar machines. However, if you have data only from time of failure, then you can use survival models. If failure data is not available but you have knowledge of a safety threshold, you can use degradation models. The video gives an overview of all these models and then discusses one of these techniques - the similarity model - in more detail with an aircraft engine example.
Related Resources:
- Overcoming Four Common Obstacles to Predictive Maintenance: bit.ly/2GoZjyI
- NASA Prognostics Data Repository: go.nasa.gov/2t...
- Check out this example to explore how data reduction is performed: bit.ly/2teJmlc
- RUL Estimation Using RUL Estimator Models: bit.ly/2te1awP
- MATLAB and Simulink for Predictive Maintenance: bit.ly/2Tp2yLq
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This is so cool. I'm gonna figure this shit out and help a bunch of folks.
I can't thank you enough. Very good videos.
pronunciation is perfect.
Very professional presentation
[HELP ME]
6:21 What is the best way to do this using python? Scikit-learn? What model?
I have looked at the dataset but i failed to find the lables ( RUL time ) in datasets. can you help me to find where is the RUL field is?
Could you please give me the link of dataset for RUL prediction? I need dataset.
what machine learning or deep learning algorithms are most relevant in predictive maintenance, like for mechanical machinery? I want to focus on them.
thank you in advance.
do u found that answer?
For regression task (to predict RUL) i think it's possible to use Kalman filter with state-space model from math statistics (it works good with real-time data since it can vary regression coefficients in time) and LSTM neural network from deep learning
How do we know the actual RUL value? Don't we only have historical data?
In normal case yes. But as I understood here we have a complete data of the system including the failure and we are testing our predictive maintenance algorithm whether it can predict RUL correct or not
She is lovely ❤️
you are super and will you marry me!
She can only marry you if she predicts you have enough RUL
@@abhijitdas8617 Haha🤣🤣🤣🤣 not sure how much is left after 3 years