Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets

no code implementations • 13 Jun 2022 • Mustafa Abdallah, Byung-Gun Joung, Wo Jae Lee, Charilaos Mousoulis, John W. Sutherland, Saurabh Bagchi

In this paper, we analyze four datasets from sensors deployed from manufacturing testbeds.

Anomaly Detection Time Series +2

Robust Audio Anomaly Detection

no code implementations • 3 Feb 2022 • Wo Jae Lee, Karim Helwani, Arvindh Krishnaswamy, Srikanth Tenneti

The presented approach doesn't assume the presence of labeled anomalies in the training dataset and uses a novel deep neural network architecture to learn the temporal dynamics of the multivariate time series at multiple resolutions while being robust to contaminations in the training dataset.

Anomaly Detection Time Series +1

Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems

no code implementations • 11 Feb 2021 • Mustafa Abdallah, Wo Jae Lee, Nithin Raghunathan, Charilaos Mousoulis, John W. Sutherland, Saurabh Bagchi

While there is a rich literature on anomaly detection in many IoT-based systems, there is no existing work that documents the use of ML models for anomaly detection in digital agriculture and in smart manufacturing systems.

Anomaly Detection Time Series Analysis +1