Smart Structures and Systems

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Sensor fault diagnosis for bridge monitoring system using similarity of symmetric responses

  • Xu, Xiang (School of Transportation, Southeast University) ;
  • Huang, Qiao (School of Transportation, Southeast University) ;
  • Ren, Yuan (School of Transportation, Southeast University) ;
  • Zhao, Dan-Yang (School of Transportation, Southeast University) ;
  • Yang, Juan (Department of Engineering, Nanjing No.3 Yangtze River Bridge Ltd)
  • Received : 2018.06.11
  • Accepted : 2019.01.30
  • Published : 2019.03.25
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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.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Southeast University, China Scholarship Council

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