Deflection Sensors Fault Time Locating Based on Kalman Filtering and Data Fusion

Abstract:

Traditional research on sensor fault are usually confined to fault space locating, however, it’s very necessary to determine the time that the fault occurrence for subsequent data processing and to guarantee the normal operation of the monitoring system. Therefore, this paper proposes a method of sensors fault time locating. First of all, use Kalman filter to process the sensor data, then define the support level of sliding window correlation to sample correlation, combining with multi sensors data fusion, so as to identify the accurate time point of fault. Combine the deflection sensors data from Caiyuanba bridge, simulating four common faults. The results show that the error of located time is less than the width of the sliding window.

• Periodical: Applied Mechanics and Materials (Volume 743)
• Pages: 820-823
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.743.820
• Online since: March 2015
• Authors: S.R. Hu, J.K. Zhang, K. Liang, M. Bao
• Keywords: Data Fusion, Kalman Filter, Sliding Window, Support Level, Time Locating

References

[1] Houssam Toutanji, Yong Deng. Deflection and crackwidth prediction of concrete beams reinforced with glass FRP rods[J]. Construction and Building Materials(S0950-0618), 2003, 17(1): 69-74.

DOI: 10.1016/s0950-0618(02)00094-6

[2] Dewei Chen, Xinran Li, Wenjun Yang. The new deflection test method in the bridge health monitoring system [C] The 16th session of the National Bridge Conference, 2004: 564-569.

[3] Shunren Hu. Dynamic threshold interval prediction for optoelectronic imaging deflection system[J]. Transducer and Microsystem Technologies, 2009, 28(8): 59-62.

[4] Yongyuan Qin, Hongyue Zhang, Shuhua Wang, etc. Kalman filter and the principle of integrated navigation[M]. Xian: Northwestern Polytechnical University Press, (2012).

[5] John M. Richardson, Kenneth A. March. Fusion of Multi sensor Data[J]. The International Journal of Robotics Research, 1988, 7(6): 78—96.

[6] Chongquan Zhong, Liyong Zhang, Suying Yang, etc. Based on the least squares principle of multi-sensor weighted data fusion algorithm [J]. Chinese Journal of Scientific Instrument, 2003, 24(4): 427-430.

[7] Yi-Hung Liao, Jung—Chuan Chou. Weighted data fusion use for ruthenium 364 dioxide thin film pH array electrodes[J]. IEEE SENSORS JOURNAL, JULY 2009: 842-848.

DOI: 10.1109/jsen.2009.2024045

[8] Yu Zhang, Jinhe Ran. Dynamic Weighted Track Fusion Algorithm Based on Track Comparability Degree[J]. Information Theory and Information Security(ICITIS), 2010: 710-713.

DOI: 10.1109/icitis.2010.5689499