Autoencoder-based anomaly detection for sensor data
Updated 3 Jul 2020
This demo highlights how one can use an unsupervised machine learning technique based on an autoencoder to detect an anomaly in sensor data (output pressure of a triplex pump). The demo also shows how a trained auto-encoder can be deployed on an embedded system through automatic code generation. The advantage of auto-encoders is that they can be trained to detect anomalies with data representing normal operation, i.e. you don't need data from failures.
Antti (2022). Autoencoder-based anomaly detection for sensor data (https://github.com/aloytyno/Autoencoder-based-anomaly-detection-for-sensor-data/releases/tag/1.1), GitHub. Retrieved .
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