A deep-learning-based approach towards identifying combined faults in structural health monitoring

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.

Subjects

classification networks

Identification of combined sensor faults

long short-term memory

sensor fault diagnosis

structural health monitoring

MLE@TUHH

DDC Class

624.1: Structural Engineering

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publishedVersion

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https://creativecommons.org/licenses/by/4.0/

Name

166_manuscript.pdf

Type

Main Article

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399.27 KB

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Adobe PDF

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A deep-learning-based approach towards identifying combined faults in structural health monitoring