DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series

TL;DR

DeepTimeAnomalyViz is a web-based visualization tool designed to facilitate the exploration, comparison, and optimization of deep learning models for anomaly detection in industrial time-series data, addressing practical implementation challenges.

Contribution

The paper introduces DeTAVIZ, a novel web interface that streamlines the evaluation and tuning of pretrained deep learning models for industrial anomaly detection tasks.

Findings

Enables quick comparison of multiple models and post-processing options.

Supports manual optimization towards specific metrics.

Facilitates feasibility assessment of DL-based anomaly detection.

Abstract

Industrial processes are monitored by a large number of various sensors that produce time-series data. Deep Learning offers a possibility to create anomaly detection methods that can aid in preventing malfunctions and increasing efficiency. But creating such a solution can be a complicated task, with factors such as inference speed, amount of available data, number of sensors, and many more, influencing the feasibility of such implementation. We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. Provided with a pool of pretrained models and simulation results, DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.