Live demonstration : autoencoder-based predictive maintenance for IoT
This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from se...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/138250 |
| Summary: | This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection. |
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