This paper deals with a distributed acoustic emission sensing method, which is especially suitable for piezoelectric paint.
Piezoelectric paint is a composite piezoelectric material that is comprised of tiny piezoelectric particles randomly
dispersed within a polymer matrix phase. An overview of the distributed acoustic emission sensing method for defect
monitoring is given in this paper. The use of piezoelectric materials for ultrasonic signal measurements is next discussed
along with a series of ultrasonic tests performed to verify the ultrasonic sensing capability of piezoelectric paint. To
examine the mechanism of the distributed acoustic emission sensing method for crack initiation detection, the results of
a finite element simulation based study is presented in this paper. The finite element model used in the parametric study
is calibrated with experimental data. The effect of sensor numbers included in the array has been studied using both
simulation and experimental data. Based on the preliminary results of this study, piezoelectric paint sensor appears to
hold a potential for use in on-line monitoring of cracks such as those caused by fatigue in metal structures although more
work is still needed before successful practical application can be made.
The inconsistency in the data handling capacity of the components of an on-line SHM system has impeded its effective
use. To address this problem, this paper presents a new concept for integrated SHM system with several special
functional modules such as sensor data compression, interactive data retrieval and structural knowledge discovery. In
such an integrated SHM system, streamlined data flow is used as a unifying thread to integrate the individual
components of on-line SHM system. Adoption of this new concept will enable the design of on-line SHM system with
more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of
vibration-based SHM system, real sensor data from an on-line SHM system comprising a laboratory size steel bridge
structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results
clearly demonstrated the prominent performance characteristics of integrated SHM system including rapid data access,
interactive data retrieval and knowledge discovery of structural conditions on a global level.
In this paper, the performance of a prediction error method (PEM)-based second order structural identification method is studied through a series of vibration test. The concerned structural identification method employs a prediction error method to identify the parameters of ARMAX/ARMA models that are formulated in a stochastic state space framework. This system identification method can be used to identify second order structural parameters such as mass, stiffness, damping ratios directly from measured vibration data. To evaluate the effectiveness of this PEM-based structural identification method, vibration data collected from a 3-storey model structure is used. Two series of vibration tests were carried out: in the first test series, dynamic load applied at the roof of the building is measured; the second test series involves base excitation of the model building. The results of this experimental study indicate that the PEM-based structural system identification technique is able to identify the second order structural parameters and locate the damages reasonably well. Therefore, the PEM-based structural identification method has a potential to be used for damage detection in structural health monitoring applications.
Current trend in structural condition monitoring system is towards the use of a large number of networked sensors, which correspondingly generate huge amount of sensor data. High data rates pose challenges in data transmission, storage search and remote retrieval, especially for wireless communication network. To address this problem, innovative sensor data compression techniques are needed to reduce the sensor data size. Lossy data compression techniques have the potential to achieve high compression rates but suffer the problem of signal distortion. This paper presents a waveletbased lossy compression method targeted for vibration sensor data. The trade-off between compression rate and signal distortion due to lossy compression is discussed in this paper. The effect of wavelet-based lossy data compression on both the time domain and frequency domain characteristics of vibration signals is studied. Real sensor data collected from a scaled two-story building structure using wireless accelerometer has been used in this study.
The implementation of smart structures technology in the design, construction and maintenance of civil and mechanical systems have been shown beneficial to the performance enhancement, operating efficiency and reliability of structural systems. However, most of today's engineering students are unaware of the remarkable properties of smart sensors and many applications of smart structures technology. It is thus desirable to prepare the future engineers of the society for the cutting-edge technologies in smart structures, for which they may see broad application in their generation. Pioneering work in incorporating smart structures technologies into civil engineering curriculum has been done by the writer at Lehigh University and is described in this paper. In particular, a graduate-level course entitled "Smart Structural Systems" has been taught in the Spring Semester of Year 2004 at Lehigh University. To better convey the course material to students, a smart structures test-bed, which is used not only to showcase various technological aspects of a smart structural system but also offer students an opportunity to gain hands-on experience by doing experiments has been under development at Lehigh University. The hands-on experience that could be developed with the smart structures test-bed is believed being essential for students to have a good understanding and mastering of the smart structures technologies.
Sensors, which collect data for further information processing, are core component of any viable structural health monitoring system. Continuous on-line structural health monitoring can be achieved through the use of advanced sensors developed for real-time structural health monitoring applications. To overcome the problems associated with traditional piezoelectric ceramics, a polymer-based piezoelectric paint material has been developed and recently used for sensors. The piezoelectric paint is composed of tiny piezoelectric particles mixed within polymer matrix and therefore belongs to "0-3" piezoelectric composite. Because of the electro-mechanical coupling properties of piezoelectric paint, the dynamic responses of host structures can be monitored by measuring the output voltage signals from the piezoelectric paint sensor. Piezoelectric paint sensors hold a great potential for dynamic strain sensing applications due to the ease with which their mechanical properties can be adjusted, low fabrication cost, ease of implementation, and conformability to curved surface Additionally, a novel surface crack detection technique has been conceived and validated experimentally, in which cracks of the host structure is detected by observing the measured signals from an piezoelectric paint sensor with multi-electrode configuration. This paper presents this piezoelectric paint-based crack monitoring method as well as validation test data. The piezoelectric paint sensor is ideal for surface crack detection in locations with complex geometry, such as welded joints, which conventional sensors are ill equipped to do.