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Anomaly Detection in Industrial Robots by Rodolfo Betanzos is a document available to read on EtoBox.
This document introduces a method for unsupervised anomaly detection of industrial robots using a sliding-window convolutional variational autoencoder (SWCVAE). The SWCVAE is designed to perform real-time anomaly detection on multivariate time series data collected from robots. It can capture both temporal dependencies within the time series data and inter-correlations between different metrics. The method is tested on a KUKA KR6R 900SIXX industrial robot and is able to successfully detect anomalies. The pr
- Author
- Rodolfo Betanzos
- Language
- EN