Autoencoder-based anomaly detection for sensor data

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.