Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencoder

no code implementations • 20 May 2022 • Susumu Naito, Yasunori Taguchi, Kouta Nakata, Yuichi Kato

This paper focuses on anomaly detection for multivariate time series data in large-scale fluid handling plants with dynamic components, such as power generation, water treatment, and chemical plants, where signals from various physical phenomena are observed simultaneously.

Anomaly Detection Time Series +1