Wireless High-Resolution Acceleration Measurements for Structural Health Monitoring of Wind Turbine Towers
Open accessAbstract
Structural health monitoring (SHM) will be pivotal for safe and economic operation of wind turbines. Timely discovery of changes within the structure and means of prediction of required maintenance will reduce production costs of electricity and catastrophic failures. Long-term structural acceleration recording can support damage detection on turbine towers and document progression of fatigue. Conventional acceleration recordings are based on wired sensor nodes at fixed positions with privileged accessibility and electric power supply. However, such positions might be near vibration nodes and not necessarily experience the maximum vibration amplitude. Shifts in eigenfrequencies can be an indicator of changes in structural stiffness, hence damage, but also be caused by environmental effects, e.g., temperature. Damages generate local effects while the structure’s vibration spectrum is a global evaluation. If a sensor is close to the location of damage, the probability of detection is increased. Wireless sensors powered by batteries are advantageous for this task as they are independent of cabling for power supply and data transmission. Such monitoring of turbine tower structures is not common in practice and requires new data-enabled techniques to discover deviations from the optimal way of wind turbine operation. This paper proposes a new approach using wireless high-resolution acceleration measurement sensor nodes, exploiting the vibration response of wind turbine towers. Influences of acceleration resolution and sensor node locations onto the accuracy of eigenfrequency determination are demonstrated. A comparison between acceleration recordings by wireless sensor nodes and their wired counterparts is presented to prove the equivalence of the wireless sensing method. Finally, new data compression techniques used with the sensor nodes are discussed to reduce wireless transmission to a minimum.
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The authors in [15] do not provide any data about power consumption and estimated battery lifetime.
“Despite the authors deploying the system on a wind turbine, they do not provide any data about the power consumption and estimated battery lifetime.”
From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [15]
In [15], the authors propose a flexible battery-powered wireless system for SHM of wind turbine towers exploiting only MEMS accelerometers.
“The authors in [15] propose a flexible solution offering a battery-powered and wireless system for SHM of wind turbine towers exploiting only MEMS accelerometers.”
From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [15]
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Research works printed but not linked: 17
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Polonelli, T., Muller, H., Kong, W., Fischer, R., Benini, L., & Magno, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3224307 · Cited by 35 · see citing works
- Authors
- Wondra, B., Malek, S., Botz, M., Glaser, S. D., & Grosse, C. U.
- Affiliations
- Technical University of Munich (DE)
- University of California, Berkeley (US)
- Corresponding author
- Wondra, Bernhard
- Venue
- Data-Enabled Discovery and Applications
- Published
- 2019-01-14
- Volume
- 3
- Issue
- 1
- ISSN
- 2510-1161 (ISSN-L)
- OpenAlex
- W2926663602
- Cited by
- 28
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 11 · 2021: 3 · 2020: 3 · 2019: 1
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Data-Enabled Discovery and Applications (publishedVersion, cc-by)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- Bundesministerium für Wirtschaft und Energie (0325795)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Aerospace Engineering › Wind Energy Research and Development
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Keywords
- wind turbine towers, structural health monitoring, wireless sensor nodes, damage detection, fatigue (material), vibration response, sensor deployment, data compression, battery-powered sensors
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