Data-driven fault diagnosis in structural health monitoring systems for civil infrastructure (feasibility study)

Problem statement
Structural health monitoring (SHM) is increasingly used to ensure functionality and serviceability of civil infrastructure. SHM for civil infrastructure comprises automated data acquisition, data analysis, data communication, and data storage. Despite the advancements in information and communication technologies, the quality of monitoring can be significantly affected by sensor faults. Most types of sensor faults that commonly occur in SHM cannot automatically be detected by SHM systems.

Project goal
This project aims at transferring a fault diagnosis concept into practice, which has been proposed and theoretically validated by the Chair of Computing in Engineering in previous research. By means of data-driven fault diagnosis, SHM systems will be enabled to automatically perform fault diagnosis tasks to detect and isolate different types of sensor faults. Unlike model-based approaches, the data-driven approach pursued in this project does not require physics-based computer models, which may be highly complex and computationally intensive. Instead, automated fault diagnosis is performed solely based on measurement data recorded from civil infrastructure.

Implementation
A system model for fault detection and isolation based on an artificial intelligence approach will be implemented. In addition, a decision logic will be designed to enable SHM systems automatically making decisions with respect to fault identification. Finally, the modules are integrated into an existing SHM system and the feasibility is validated through field tests. Implemented on a railway bridge, the approach will be extended to allow automated train detection by pattern recognition conducted on the sensor data recorded from the bridge. 

Railway bridge near Weimar (Source: MKP GmbH)
Railway bridge near Weimar (Source: MKP GmbH)

Project type
Collaborative project
Federal Ministry of Transport and Digital Infrastructure (BMVI)
Principal investigator: Professor Dr. Kay Smarsly

Duration
05/2019 – 04/2020

Project partners

  • Bauhaus University Weimar, Chair of Computing in Civil Engineering: Providing theoretically proven approaches for automatic, data-driven fault diagnosis to be transferred into engineering practice
  • MKP GmbH Weimar: Engineering supervision and provision of structural health monitoring systems
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Project-related publications

  • Smarsly, K., Mthunzi, E. M., Hahn, O. & Planer, J., 2019. Validation of an ultra-low-cost wireless structural health monitoring system for civil infrastructure. In: Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM). Stanford, CA, USA, 09/10/2019 (accepted).
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  • Steiner, M., Legatiuk, D. & Smarsly, K., 2019. A support vector regression-based approach towards decentralized fault diagnosis in wireless structural health monitoring systems. In: Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM). Stanford, CA, USA, 09/10/2019 (accepted).
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Contact
Professor Dr. Kay Smarsly
Bauhaus University Weimar
Computing in Civil Engineering
Coudraystraße 7, Room 518
99423 Weimar
Email: kay.smarsly[at]uni-weimar.de

Henrieke Fritz, B.Sc.
Bauhaus University Weimar
Computing in Civil Engineering
Coudraystraße 7, Room 521
E-Mail: henrieke.fritz[at]uni-weimar.de 

Contact person at BMVI:
Federal Ministry of Transport and Digital Infrastructure (BMVI)
Dr. Christopher Kaan
Referat DG 21
Tel.: +49 30 18300-6218
E-Mail: christopher.kaan[at]bmvi.bund.de