Sensor Fault Diagnosis for Structural Health Monitoring Based on Statistical Hypothesis Test and Missing Variable Approach
Publication: Journal of Aerospace Engineering
Volume 30, Issue 2
Abstract
Using structural monitoring data collected from a sensor network to assess the health condition of a monitored structure relies on the accurate operation of the sensors and therefore could be affected by various sensor faults. This paper presents a sensor-fault detection and isolation approach with application to structural health monitoring. Principal-component analysis (PCA) is first applied to model the fault-free history monitoring data to generate uncorrelated residuals, which can be seen as the projection of the additional measurement noise into the residual subspace of the PCA transform. Then, under the assumption that the measurement noise is Gaussian distributed, a statistical hypothesis test model is established for the subsequent sensor-fault detection procedure, after that two fault detectors are deduced through the rejection of the null hypothesis. Next, the missing variable approach is used to establish an isolation index to identify the specific faulty sensor. A benchmark structure developed for bridge health monitoring is adopted to validate and demonstrate the performance of the proposed method, and the analysis results indicate that the method is effective in detecting and isolating both bias and drift sensor faults.
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Acknowledgments
This research work was jointly supported by the 973 Program (Grant No. 2015CB060000), the National Natural Science Foundation of China (Grant Nos. 51421064, 51478081, 51222806), the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130041110031), and the Science Fund for Distinguished Young Scholars of Dalian (2014J11JH125).
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Published In
Journal of Aerospace Engineering
Volume 30 • Issue 2 • March 2017
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Mar 10, 2015
Accepted: Sep 14, 2015
Published online: Dec 21, 2015
Discussion open until: May 21, 2016
Published in print: Mar 1, 2017
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