A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring
https://orcid.org/0000-0001-6746-1758
https://orcid.org/0000-0002-3482-2251
https://orcid.org/0000-0002-4204-6547
https://orcid.org/0000-0001-7228-3503
Artificial intelligence (AI) algorithms have proven effective in implementing sensor fault diagnosis (FD) for wireless structural health monitoring (SHM). However, FD models based on AI are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. This paper proposes a decentralized framework for sensor fault diagnosis in wireless SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Within the decentralized framework, FD models are embedded into wireless sensor nodes to ensure that the data collected from engineering structures is fault-free. Thus, only the condition of SHM systems, instead of raw data, is transmitted from SHM systems to centralized servers via Internet-of-Things communication. To validate the decentralized framework proposed in this paper, an SHM system is implemented using (i) a portable main station containing the FD models and (ii) four tailor-made wireless sensor nodes equipped with microcontrollers and accelerometers deployed on a test structure. The results of the validation tests show that the SHM system successfully collects acceleration data and diagnoses, in real-time, sensor faults that are inserted into the sensor nodes. In future work, the decentralized framework and the SHM system presented in this paper may be deployed on a bridge for structural condition assessment, while ensuring early detection of sensor faults.
AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring
https://orcid.org/0000-0001-6746-1758
https://orcid.org/0000-0002-3482-2251
https://orcid.org/0000-0002-4204-6547
https://orcid.org/0000-0001-7228-3503
Artificial intelligence (AI) algorithms have proven effective in implementing sensor fault diagnosis (FD) for wireless structural health monitoring (SHM). However, FD models based on AI are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. This paper proposes a decentralized framework for sensor fault diagnosis in wireless SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Within the decentralized framework, FD models are embedded into wireless sensor nodes to ensure that the data collected from engineering structures is fault-free. Thus, only the condition of SHM systems, instead of raw data, is transmitted from SHM systems to centralized servers via Internet-of-Things communication. To validate the decentralized framework proposed in this paper, an SHM system is implemented using (i) a portable main station containing the FD models and (ii) four tailor-made wireless sensor nodes equipped with microcontrollers and accelerometers deployed on a test structure. The results of the validation tests show that the SHM system successfully collects acceleration data and diagnoses, in real-time, sensor faults that are inserted into the sensor nodes. In future work, the decentralized framework and the SHM system presented in this paper may be deployed on a bridge for structural condition assessment, while ensuring early detection of sensor faults.
AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring
https://orcid.org/0000-0001-6746-1758
https://orcid.org/0000-0002-3482-2251
https://orcid.org/0000-0002-4204-6547
https://orcid.org/0000-0001-7228-3503
Artificial intelligence (AI) algorithms have proven effective in implementing sensor fault diagnosis (FD) for wireless structural health monitoring (SHM). However, FD models based on AI are computationally expensive and require large amounts of raw sensor data to be transmitted to centralized servers. This paper proposes a decentralized framework for sensor fault diagnosis in wireless SHM systems based on the concept of Artificial Intelligence of Things (AIoT). Within the decentralized framework, FD models are embedded into wireless sensor nodes to ensure that the data collected from engineering structures is fault-free. Thus, only the condition of SHM systems, instead of raw data, is transmitted from SHM systems to centralized servers via Internet-of-Things communication. To validate the decentralized framework proposed in this paper, an SHM system is implemented using (i) a portable main station containing the FD models and (ii) four tailor-made wireless sensor nodes equipped with microcontrollers and accelerometers deployed on a test structure. The results of the validation tests show that the SHM system successfully collects acceleration data and diagnoses, in real-time, sensor faults that are inserted into the sensor nodes. In future work, the decentralized framework and the SHM system presented in this paper may be deployed on a bridge for structural condition assessment, while ensuring early detection of sensor faults.
AIoT-enabled decentralized sensor fault diagnosis for structural health monitoring
Chillón Geck, Carlos (author) / Al-Zuriqat, Thamer (author) / Elmoursi, Moayed (author) / Dragos, Kosmas (author) / Smarsly, Kay (author) / TUHH Universitätsbibliothek (host institution)
2024
Conference paper
Electronic Resource
English
DDC:
624
Adaptive fault diagnosis for simultaneous sensor faults in structural health monitoring systems
| DataCite | 2023
Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems
| DOAJ | 2023
Skin-like Sensor Enabled Bridge Structural Health Monitoring System
| British Library Conference Proceedings | 2016