Detecting anomalies and de-noising monitoring data from sensors: A smart data approach |
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| Affiliation: | 1. Department of Civil and Building Systems, Technische Universität Berlin, Gustav-Meyer-Allee 25, 13156 Berlin, Germany;2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;3. School of Civil and Mechanical Engineering, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia;1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing, PR China;2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, PR China;3. State Key Laboratory of Virtual Reality Technology and System, Beijing, PR China;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China;3. National NC System Engineering Research Center, Huazhong University of Science and Technology, Wuhan 430074, China;1. ISAE-SUPMECA, Quartz Laboratory, Saint-Ouen, France;2. Roberval Laboratory, University of Technology of Compiègne, Compiègne, France;3. Laboratory of Mechanics of Sousse, National Engineering School of Sousse, University of Sousse, Sousse, Tunisia;1. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Beijing Xinghang Mechanical-Electrical Eqiupment Co., Ltd., Beijing 100074, China;3. AVIC Manufacturing Technology Institute, Beijing 100024, China;4. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China;1. School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, PR China;2. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, PR China |
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| Abstract: | 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. Our research aims to address 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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| Keywords: | Anomaly Deep pit foundations De-noise Detection Smart data Safety risks |
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