Self-Supervised Modular Architecture for Multi-Sensor Anomaly Detection and Localization
In this paper, we propose a novel modular architecture for self-supervised multi-sensor anomaly detection and localization. The framework consists of a spatio-temporal encoder for representation learning, a decoder for latent reconstruction, a predictive memory network for sub-sequence pattern...
- År
- 2024
- Type
- Vitenskapelig Kapittel/Artikkel/Konferanseartikkel