Estimating Remaining Useful Life (RUL) | Predictive Maintenance

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/2tcU4Zu - 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 -------------------------------------------------------------------------------------------------------- Get a free product Trial: goo.gl/ZHFb5u Learn more about MATLAB: goo.gl/8QV7ZZ Learn more about Simulink: goo.gl/nqnbLe See What's new in MATLAB and Simulink: goo.gl/pgGtod © 2019 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.

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