Data-driven sensor fault diagnosis for vibration-based structural health monitoring under ambient excitation

In vibration-based structural health monitoring (SHM), the early detection of sensor faults is key to preventing false alarms and misleading conclusions on the condition of monitored structures. Since sensor networks are exposed to hostile environments, they are prone to unexpected errors that might...

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Bibliographic Details
Published in:	Measurement : journal of the International Measurement Confederation Vol. 237; p. 115232
Main Authors:	Lydakis, Emmanouil, Koss, Holger, Brincker, Rune, Amador, Sandro D.R.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 30-09-2024
Subjects:	Sensor fault detection Sensor networks Structural health monitoring Vibration testing Sensor networks Structural health monitoring Sensor fault detection Vibration testing
Online Access:	Get full text
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Summary:	In vibration-based structural health monitoring (SHM), the early detection of sensor faults is key to preventing false alarms and misleading conclusions on the condition of monitored structures. Since sensor networks are exposed to hostile environments, they are prone to unexpected errors that might influence the quality of measured data. This paper proposes a novel method for detecting and isolating faulty sensors from vibration response data by establishing an overdetermined system between the measured signals and the actual motion. The method assumes a rigid body motion of the monitored system, describable by a limited number of degrees of freedom (DOFs), to define the overdetermined relation between the sensor outputs and the system’s DOFs. The concept is later extended to systems not governed by rigid body motions by considering their vibration mode shapes. The robustness of the proposed methodology is demonstrated using vibration response data from an experimental monitoring campaign. •Sensor fault diagnosis method directly applicable to raw vibration responses.•Dense sensor networks provide sufficient information for isolating faulty signals.•The proposed method accurately detects continuous-time sensor faults.•Sensor faults in vibration data negatively influence modal parameter estimation.
ISSN:	0263-2241
DOI:	10.1016/j.measurement.2024.115232