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: [ Ссылка ]
- NASA Prognostics Data Repository: [ Ссылка ]
- Check out this example to explore how data reduction is performed: [ Ссылка ]
- RUL Estimation Using RUL Estimator Models: [ Ссылка ]
- MATLAB and Simulink for Predictive Maintenance: [ Ссылка ]
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