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Smart Structures and Systems Volume 23, Number 3, March 2019 , pages 279-293 DOI: https://doi.org/10.12989/sss.2019.23.3.279 |
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Sensor fault diagnosis for bridge monitoring system using similarity of symmetric responses |
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Xiang Xu, Qiao Huang, Yuan Ren, Dan-Yang Zhao and Juan Yang
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| Abstract | ||
| To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy\'s information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data. | ||
| Key Words | ||
| structural health monitoring; sensor fault diagnosis; similarity; symmetric structure responses; multi-sensor information fusion; evidential reasoning | ||
| Address | ||
| Xiang Xu, Qiao Huang, Yuan Ren and Dan-Yang Zhao: School of Transportation, Southeast University, Sipailou, Xuanwu District, Nanjing 210-096, People | ||