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Erschienen in: Cluster Computing 4/2019

15.12.2017

Sensor fault localization with accumulated residual contribution rate for bridge SHM

verfasst von: Lili Li, Liangliang Zhang, Gang Liu, Qing Li, Xing An

Erschienen in: Cluster Computing | Sonderheft 4/2019

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Abstract

A sensor fault localization method based on the accumulated residual contribution rate is proposed based on the problems of damage false alarm and high false alarm rate caused by sensor fault in bridge structure health monitoring system. Based on the basic principle of principal component analysis, the data collected by the sensor under the vehicle load or the ground pulsation excitation are divided into the main element space and the residual space, and the fault is detected by SPE statistic. Furthermore, the residual contribution value is further deduced, and the accumulated residual contribution rate index is proposed. It improves residual contribution graph, also improves the accuracy of fault location, and sensor fault location is extended to simultaneously locate two fault sensors. The Three-span continuous beam of numerical example and actual calculation example result show that the accumulated residual contribution rate not only locates the single sensor fault, but also accurately locates simultaneous double sensors fault position, thus providing a new method for the maintenance of bridge health monitoring system.

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Metadaten
Titel
Sensor fault localization with accumulated residual contribution rate for bridge SHM
verfasst von
Lili Li
Liangliang Zhang
Gang Liu
Qing Li
Xing An
Publikationsdatum
15.12.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1456-5

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