Anomaly detection, warning system and real time simulation in predictive maintenance using LSTM and CNN
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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.
Prateek Rai on 9 Oct 2021
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.