Integration of multi-type senor data, which is mutual complementary, is a potential way to improve the accuracy and
robustness of structural damage detection method. However, the effect of damage diagnosis based on multi-sensor data
integration depends on the optimal sensor placement for multi-sensor data integration. Therefore, in the paper, an
innovative optimal sensor placement based on sensitivity is proposed to determine the number and locations of three
kinds of sensors including accelerometers, Fiber Brag Gauges (FBG), which are generally applied in vibration tests, and
piezoelectric (PZT) sensors, which are commonly used in active sensing-based structural health monitoring. With
considering the boundary effect and uncertainty caused by environment, the sensitivity-based object function to detect
every possible damage location is established. Computational simulation on a fixed-supported steel thin plate-like
structure is implemented to evaluate minimum sensor number of accelerometers, FBG and PZT according to methods
above. After that, the optimal locations and number for three kinds of sensors are calculated.
KEYWORDS: Sensors, Bridges, Sensor networks, Data acquisition, Structural health monitoring, Data communications, Signal attenuation, Wireless communications, Wavelets, Reliability
In a wireless sensor network, data loss often occurs during the data transmission between wireless sensor nodes and the
base station, which decreases the communication reliability in wireless sensor network applications. Errors caused by
data loss inevitably affect the data analysis of the structure and subsequent decision making. This paper proposed an
approach to recover lost data in a wireless sensor network based on the compressive sampling (CS) technique. The main
idea in this approach is to project the transmitted data from x onto y, where y is the linear projection of x on a random
matrix. The data vector y is permitted to lose part of the original data x in wireless transmissions between the sensor
nodes and the base station. After the base station receives the imperfect data, the original data vector x can be
reconstructed based on the data y using the CS method. The acceleration data collected from the vibration test of
Shandong Harbin Sifangtai Bridge by wireless sensors is used to analyze the data loss recovery ability of the proposed
method.
As a key problem of the vibration-based damage detection, many damage indexes were developed in recent years, but a
systematic and effective method to evaluate those damage indexes is not available till now. Therefore, a new assessment
method by sensitivity from damage indexes to stiffness, adaptation to noise, ability of correct identification based on
incomplete information and locality indicating locations of damage precisely is proposed in this paper to reflect various
main problems in the damage detection and choose the proper damage index. The numerical example results show that
conclusions drawn from proposed method as a foreordain way is consistent with common conclusions of previous study.
The assessment method containing four indexes to qualify characteristic of indicators has bright prosperity in large
structures for many important problems in practice are considered.
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