Anomaly detection, warning system and real time simulation in predictive maintenance using LSTM and CNN

4 次查看（过去 30 天）
Ng Yong Jie
Ng Yong Jie 2021-10-6
评论： Prateek Rai 2021-10-9
I am currently working on a project regarding predictive maintenance with LSTM and CNN. The algorithms are good to run. However, my task now is to add few features to the project. The features are anomaly detection, warning system and deployment of the algorithm to perform live data simulation.
  2 个评论
Arkadiy Turevskiy
Arkadiy Turevskiy 2021-10-6
编辑：Arkadiy Turevskiy 2021-10-6
Sounds like a cool project. Is that for school or work?
What are your already developed algorithms doing: fault classification, remaining useful life estimation?
You might be interested to take a look at this video where we went through a cse study of using autoencoder for anomaly detection.
What is the question you wanted to get an answer to?
Ng Yong Jie
Ng Yong Jie 2021-10-7
Hi, thanks for your reply!
It is a project for my university.
The developed algorithms is doing remaining useful life prediction using LSTM and CNN.
Thanks for the link for autoencoder. Will check it out!
My question is how should I proceed to enable the algorithm to be fed with live data for real time simulation? I am currently reading about digital twin but I am not sure if that is a right direction.
And the next question, is there any suggestion on how to implement an early warning system to the algorithm?
Thanks.

请先登录，再进行评论。

请先登录，再回答此问题。

回答（1 个）

Prateek Rai
Prateek Rai 2021-10-9
To my understanding, you have created a deep learning model for predictive maintenance with LSTM and CNN and want to add anomaly detection, warning system, and real time simulation functionalities.
For anomaly detection, you have to first set the criteria as to what output values will be distinguished as an anomaly. Based on that you have to develop your warning system.
After you are done with both the steps, then you can proceed with deployment to enable the algorithm to be fed with live data for real time simulation.
For deployment, you can refer to Prototype Deep Learning Networks on FPGA MathWorks Documentation page to learn more on deploying deep learning networks onto target FPGA and SoC boards.
  2 个评论
Prateek Rai
Prateek Rai 2021-10-9
It depends on your model. Anomaly detection depends on what activity you think anomaly is. For example, if you are reconstructing the input back as output then anomaly depends on the mean square error between input and output. Another example is what I stated in my answer, if you want your prediction to be less than a threshold then anomaly would be the case when output is more than that threshold.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Deep Learning Toolbox 的更多信息

标签

产品

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by