2021/09/21 by Błażej Leporowski, Leporowski, Błażej, Casper Worm Hansen +4
Computer Science · #Anomaly Detection Techniques and Applications #Data Stream Mining Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting #cs.LG
paper · pdf · doi:10.48550/arxiv.2109.10082
arxiv created 2021/09/21 · openalex publication_date 2021/09/21 · arxiv updated 2021/09/22 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.