A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A deep-learning-based approach towards identifying combined faults in structural health monitoring
https://orcid.org/0009-0000-6471-5919
https://orcid.org/0000-0002-3482-2251
https://orcid.org/0000-0002-4204-6547
https://orcid.org/0000-0001-6746-1758
https://orcid.org/0000-0001-7228-3503
Fault diagnosis (FD), encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring correction of sensor faults that may corrupt or invalidate monitoring data. While sensor fault identification has received scarce attention within FD of SHM systems, recent methods have been proposed for identifying single sensor faults. Nonetheless, real-world SHM systems are prone to combined faults, i.e. different faults that affect individual sensors simultaneously. Identifying combined sensor faults is essential for improving the quality of FD and for gaining insight into the causes of sensor faults. This paper presents an approach for identifying combined sensor faults, referred to as ICSF approach, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of FD in SHM systems. Leveraging a recurrent neural network, specifically a long short-term memory network, a classification algorithm is implemented for mapping time-series data to combined sensor faults. The ICSF approach is validated using acceleration measurements collected by a faulty sensor from a real-world SHM system installed on a pedestrian bridge. The results demonstrate the effectiveness of the ICSF approach in identifying combined sensor faults, enhancing sensor FD in real-world SHM systems.
A deep-learning-based approach towards identifying combined faults in structural health monitoring
https://orcid.org/0009-0000-6471-5919
https://orcid.org/0000-0002-3482-2251
https://orcid.org/0000-0002-4204-6547
https://orcid.org/0000-0001-6746-1758
https://orcid.org/0000-0001-7228-3503
Fault diagnosis (FD), encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring correction of sensor faults that may corrupt or invalidate monitoring data. While sensor fault identification has received scarce attention within FD of SHM systems, recent methods have been proposed for identifying single sensor faults. Nonetheless, real-world SHM systems are prone to combined faults, i.e. different faults that affect individual sensors simultaneously. Identifying combined sensor faults is essential for improving the quality of FD and for gaining insight into the causes of sensor faults. This paper presents an approach for identifying combined sensor faults, referred to as ICSF approach, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of FD in SHM systems. Leveraging a recurrent neural network, specifically a long short-term memory network, a classification algorithm is implemented for mapping time-series data to combined sensor faults. The ICSF approach is validated using acceleration measurements collected by a faulty sensor from a real-world SHM system installed on a pedestrian bridge. The results demonstrate the effectiveness of the ICSF approach in identifying combined sensor faults, enhancing sensor FD in real-world SHM systems.
A deep-learning-based approach towards identifying combined faults in structural health monitoring
https://orcid.org/0009-0000-6471-5919
https://orcid.org/0000-0002-3482-2251
https://orcid.org/0000-0002-4204-6547
https://orcid.org/0000-0001-6746-1758
https://orcid.org/0000-0001-7228-3503
Fault diagnosis (FD), encompassing fault detection, isolation, identification and accommodation, is critical for reliable structural health monitoring (SHM) systems, ensuring correction of sensor faults that may corrupt or invalidate monitoring data. While sensor fault identification has received scarce attention within FD of SHM systems, recent methods have been proposed for identifying single sensor faults. Nonetheless, real-world SHM systems are prone to combined faults, i.e. different faults that affect individual sensors simultaneously. Identifying combined sensor faults is essential for improving the quality of FD and for gaining insight into the causes of sensor faults. This paper presents an approach for identifying combined sensor faults, referred to as ICSF approach, aiming to identify sensor faults occurring simultaneously in individual sensors using time-series data, thereby improving the quality of FD in SHM systems. Leveraging a recurrent neural network, specifically a long short-term memory network, a classification algorithm is implemented for mapping time-series data to combined sensor faults. The ICSF approach is validated using acceleration measurements collected by a faulty sensor from a real-world SHM system installed on a pedestrian bridge. The results demonstrate the effectiveness of the ICSF approach in identifying combined sensor faults, enhancing sensor FD in real-world SHM systems.
A deep-learning-based approach towards identifying combined faults in structural health monitoring
Al-Nasser, Heba (author) / Al-Zuriqat, Thamer (author) / Dragos, Kosmas (author) / Chillón Geck, Carlos (author) / Smarsly, Kay (author) / TUHH Universitätsbibliothek (host institution)
2024
Conference paper
Electronic Resource
English
DDC:
624
Identification of combined sensor faults in structural health monitoring systems
| DataCite | 2024
Deep learning-based structural health monitoring
| Elsevier | 2024
Deep Learning Methods for Vibration-Based Structural Health Monitoring: A Review
| Springer Verlag | 2024