Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025 - Volume 3 |
| Herausgeber/-innen | Álvaro Cunha, Elsa Caetano |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 473-484 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 978-3-031-96114-4 |
| ISBN (Print) | 9783031961137 |
| Publikationsstatus | Veröffentlicht - 1 Okt. 2025 |
| Veranstaltung | 11th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025 - Porto, Portugal Dauer: 2 Juli 2025 → 4 Juli 2025 |
Publikationsreihe
| Name | Lecture Notes in Civil Engineering |
|---|---|
| Band | 676 LNCE |
| ISSN (Print) | 2366-2557 |
| ISSN (elektronisch) | 2366-2565 |
Abstract
Structural health monitoring (SHM) systems rely on a network of sensors to assess the health of engineering structures that are designed to last for decades. Over time, engineering structures such as wind turbine towers can experience degradation-related damage, while the monitoring systems themselves can age and degrade, becoming less reliable. This aging of SHM systems can result in sensor faults that produce plausible but incorrect data, leading to misinterpretations of structural integrity and potentially catastrophic failures. Therefore, distinguishing between sensor faults and structural damage is critical to ensuring the reliability of SHM over the lifetime of the structure. This study presents a probabilistic sensor fault detection approach to address this issue. The sensor correlation within the sensor network is analyzed and sensor faults are detected using Mahalanobis distance. The sensor fault detection is validated on a real support structure in the field, which is realized as a 9-m-high lattice mast under real environmental conditions. The results show that different sensor faults such as bias and drift can be accurately distinguished from structural damage, whereas gain and noise increase are not detectable. The advantage of this approach is that a generalized threshold can be defined based on the probabilistically based Mahalanobis distance, which enables automated sensor fault detection. Overall, increasing the robustness of SHM systems will significantly improve the reliability of data-based assessments, a task that is becoming increasingly important for long-lived structures.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
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Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025 - Volume 3. Hrsg. / Álvaro Cunha; Elsa Caetano. Springer Science and Business Media Deutschland GmbH, 2025. S. 473-484 (Lecture Notes in Civil Engineering; Band 676 LNCE).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Probabilistic Sensor Fault Detection in Structural Health Monitoring Systems Using Mahalanobis Distance
AU - Bartels, Jan Hauke
AU - Mett, Felix
AU - Winnewisser, Niklas
AU - Potthast, Thomas
AU - Beer, Michael
AU - Marx, Steffen
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/10/1
Y1 - 2025/10/1
N2 - Structural health monitoring (SHM) systems rely on a network of sensors to assess the health of engineering structures that are designed to last for decades. Over time, engineering structures such as wind turbine towers can experience degradation-related damage, while the monitoring systems themselves can age and degrade, becoming less reliable. This aging of SHM systems can result in sensor faults that produce plausible but incorrect data, leading to misinterpretations of structural integrity and potentially catastrophic failures. Therefore, distinguishing between sensor faults and structural damage is critical to ensuring the reliability of SHM over the lifetime of the structure. This study presents a probabilistic sensor fault detection approach to address this issue. The sensor correlation within the sensor network is analyzed and sensor faults are detected using Mahalanobis distance. The sensor fault detection is validated on a real support structure in the field, which is realized as a 9-m-high lattice mast under real environmental conditions. The results show that different sensor faults such as bias and drift can be accurately distinguished from structural damage, whereas gain and noise increase are not detectable. The advantage of this approach is that a generalized threshold can be defined based on the probabilistically based Mahalanobis distance, which enables automated sensor fault detection. Overall, increasing the robustness of SHM systems will significantly improve the reliability of data-based assessments, a task that is becoming increasingly important for long-lived structures.
AB - Structural health monitoring (SHM) systems rely on a network of sensors to assess the health of engineering structures that are designed to last for decades. Over time, engineering structures such as wind turbine towers can experience degradation-related damage, while the monitoring systems themselves can age and degrade, becoming less reliable. This aging of SHM systems can result in sensor faults that produce plausible but incorrect data, leading to misinterpretations of structural integrity and potentially catastrophic failures. Therefore, distinguishing between sensor faults and structural damage is critical to ensuring the reliability of SHM over the lifetime of the structure. This study presents a probabilistic sensor fault detection approach to address this issue. The sensor correlation within the sensor network is analyzed and sensor faults are detected using Mahalanobis distance. The sensor fault detection is validated on a real support structure in the field, which is realized as a 9-m-high lattice mast under real environmental conditions. The results show that different sensor faults such as bias and drift can be accurately distinguished from structural damage, whereas gain and noise increase are not detectable. The advantage of this approach is that a generalized threshold can be defined based on the probabilistically based Mahalanobis distance, which enables automated sensor fault detection. Overall, increasing the robustness of SHM systems will significantly improve the reliability of data-based assessments, a task that is becoming increasingly important for long-lived structures.
KW - Acceleration measurements
KW - mahalanobis distance
KW - sensor aging
KW - sensor fault detection
KW - sensor fault diagnosis
KW - structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=105019210882&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-96114-4_49
DO - 10.1007/978-3-031-96114-4_49
M3 - Conference contribution
AN - SCOPUS:105019210882
SN - 9783031961137
T3 - Lecture Notes in Civil Engineering
SP - 473
EP - 484
BT - Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025 - Volume 3
A2 - Cunha, Álvaro
A2 - Caetano, Elsa
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Conference on Experimental Vibration Analysis for Civil Engineering Structures, EVACES 2025
Y2 - 2 July 2025 through 4 July 2025
ER -