Getting Started with Predictive Maintenance

This video explains different maintenance strategies and walks you through a workflow for developing a predictive maintenance algorithm.
- Overcoming Four Common Obstacles to Predictive Maintenance: bit.ly/2GoZjyI
- MATLAB and Simulink for Predictive Maintenance: bit.ly/2Tp2yLq
Predictive maintenance lets you find the optimum time to schedule maintenance by estimating time to failure. It also pinpoints problems in your machinery and helps you identify the parts that need to be fixed. Using predictive maintenance, you can minimize downtime and maximize equipment lifetime.
- Designing Algorithms for Condition Monitoring and Predictive Maintenance: bit.ly/2GsiGae
- Using Simulink to Generate Fault Data: bit.ly/2Gnb7Bw
This video uses a triplex pump example to walk you through the predictive maintenance algorithm steps. To develop an algorithm, you need a large set of sensor data collected under different operating conditions. In cases, where sensor data is not enough, you can use simulation data that is representative of failures by creating a model of your machine and simulating faulty operating conditions. For more information on generating failure data using Simulink®, please check out the links given below.
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
© 2018 The MathWorks, Inc. MATLAB and Simulink are registered
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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.

[@MATLAB]

Correction: Between 2:59 - 3:16, the fluid viscosities shown in the image on the right should be swapped: high viscosity for Alaska, low viscosity for Texas.

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[@hisashen3855]

Thanks, this instruction give me a new concept between the Predictive maintenance(PvM) and Machine Learning(ML).

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[@causeformayhem]

Damn Matlab. This is a great presentation. I've learned so much. Thank you

[@kits1497]

Nice, it was of great help

[@kammelna]

Thanks for sharing this.
I am new in this subject but I need to know if there is any toolbox used to extract features using Matlab and some examples on it?
Regards

[@meldaulusoy8389]

Hi, Predictive Maintenance Toolbox lets you develop predictive maintenance algorithms for your system. The toolbox includes the Diagnostic Feature Designer app that lets you extract features. You may want to check out the part-4 video where we demonstrate how you can use Diagnostic Feature Designer app for feature extraction: ruclips.net/video/oDd7aEmRNpI/видео.html
Here's another video about the Diagnostic Feature Designer: ruclips.net/video/W5ljkIIz6gQ/видео.html
Also find more information on the Predictive Maintenance Toolbox product page: www.mathworks.com/products/predictive-maintenance.html

[@bilalhassani5411]

Thanks!

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This is helpful

[@aarontsui7693]

Where can I get documentation about this video? Thanks.

[@meldaulusoy8389]

Hi Aaron, can you tell me a bit more on what you're referring as documentation. If you're looking for the transcript of the video, you can find it on this page: www.mathworks.com/videos/predictive-maintenance-part-1-introduction-1545827554336.html
And for additional information on predictive maintenance, please check out the links given in the video description.

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[@naviddavanikabir]

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.

[@Music_Engineering]

I am guessing it would be time series data of some sort. You should therefore probably either use a CNN or RNN though amazon recently published a paper stating that they had the most luck with CNN's with attention, rather than using RNN's. The CNN could also be used on the frequency domain data.
You could fx say that you keep like 2048 samples back in time, use fft on that, and then use the fft and time domain data as your dataset to train on.

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@@Music_Engineering autoencoder, serch for anomaly

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