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Smart Structures and Systems Volume 20, Number 1, July 2017 , pages 43-52 DOI: https://doi.org/10.12989/sss.2017.20.1.043 |
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A sensor fault detection strategy for structural health monitoring systems |
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Chia-Ming Chang, Jau-Yu Chou, Ping Tan and Lei Wang
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| Abstract | ||
| Structural health monitoring has drawn great attention in the field of civil engineering in past two decades. These structural health monitoring methods evaluate structural integrity through high-quality sensor measurements of structures. Due to electronic deterioration or aging problems, sensors may yield biased signals. Therefore, the objective of this study is to develop a fault detection method that identifies malfunctioning sensors in a sensor network. This method exploits the autoregressive modeling technique to generate a bank of Kalman estimators, and the faulty sensors are then recognized by comparing the measurements with these estimated signals. Three types of faults are considered in this study including the additive, multiplicative, and slowly drifting faults. To assess the effectiveness of detecting faulty sensors, a numerical example is provided, while an experimental investigation with faults added artificially is studied. As a result, the proposed method is capable of determining the faulty occurrences and types. | ||
| Key Words | ||
| sensor fault detection; autoregressive modeling; a bank of Kalman estimators | ||
| Address | ||
| Chia-Ming Chang and Jau-Yu Chou: Department of Civil Engineering, National Taiwan University, Taipei 10617, Taiwan Ping Tan and Lei Wang: Earthquake Engineering Research & Test Center, Guangzhou University, Guangzhou 510405, China | ||