Detecting anomalies and de-noising monitoring data from sensors: A smart data approach

In January 2023, Timo Hartmann (↗️), coordinator of the ASHVIN project and professor at the Technical University of Berlin,  co-authored an article entitled “Detecting anomalies and de-noising monitoring data from sensors: A smart data approach“ that was published in the scientific peer-reviewed journal “Advanced Engineering Informatics”, Volume 55. 

The article details that when monitoring safety levels in deep pit foundations using sensors, anomalies (e.g., highly correlated variables) and noise (e.g., high dimensionality) exist in the extracted time series data, impacting the ability to assess risks. This research addresses the following question: How can we detect anomalies and de-noise monitoring data from sensors in real-time to improve its quality and use it to assess geotechnical safety risks? In addressing this research question, we develop a hybrid smart data approach that integrates Extended Isolation Forest and Variational Mode Decomposition models to detect anomalies and de-noise data effectively. We use real-life data obtained from sensors to validate our smart data approach while constructing a deep pit foundation. Our smart data approach can detect anomalies with a root mean square error and signal-to-noise ratio of 0.0389 and 24.09, respectively. To this end, our smart data approach can effectively pre-process data enabling improved decision-making and the management of safety risks.

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 💡 Did you know that the research conducted in the ASHVIN project has been shared across 25 different peer-reviewed publications in scientific journals or conference proceedings?

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