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| Name | Estimating Remaining Useful Life (RUL) | Predictive Maintenance |
| Video Uploader | Video From MATLAB |
| Upload Date | This Video Uploaded At 11-02-2019 14:00:04 |
| Video Discription | 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: https://bit.ly/2GoZjyI - NASA Prognostics Data Repository: https://go.nasa.gov/2tcU4Zu - Check out this example to explore how data reduction is performed: https://bit.ly/2teJmlc - RUL Estimation Using RUL Estimator Models: https://bit.ly/2te1awP - MATLAB and Simulink for Predictive Maintenance: https://bit.ly/2Tp2yLq -------------------------------------------------------------------------------------------------------- Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See What's new in MATLAB and Simulink: https://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. Dd_4rbWYgI4 |
| Category | Science & Technology |
| Tags | MATLAB | Simulink | MathWorks | ディープラーニング | 予知保全 | 異常検知 | 事後保全 | 予防保全 | deep learning | 機械学習 | machine learning | 入門 | 物体検出 | 物体検知 | 物体認識 | 人工知能 | AI | 预测性维护 | 预防性维护 | 剩余使用寿命 | RUL | 估算器模型 | 故障数据 | 安全阀值 | 机器学习 | 数字孪生 | 数据科学 | 6000745527001 |
