A critical part of structural health monitoring is accurate detection of damages in the structure. This paper presents the
results of two multi-class damage detection and identification approaches based on classification using Support Vector
Machine (SVM) and Artificial Neural Networks (ANN). The article under test was a fiber composite panel modeled by a
Finite Element Model (FEM). Static strain data were acquired at 6 predefined locations and mixed with Gaussian noise
to simulate performance of real strain sensors. Strain data were then normalized by the mean of the strain values. Two
experiments were performed for the performance evaluation of damage detection and identification. In both experiments,
one healthy structure and two damaged structures with one and two small cracks were simulated with varying material
properties and loading conditions (45 cases for each structure). The SVM and ANN models were trained with 70% of
these samples and the remaining 30% samples were used for validation. The objective of the first experiment was to
detect whether or not the panel was damaged. In this two class problem the average damage detection accuracy for
ANN and SVM were 93.2% and 96.66% respectively. The objective of second experiment was to detect the severity of
the damage by differentiating between structures with one crack and two cracks. In this three class problem the average
prediction accuracy for ANN and SVM were 83.5% and 90.05% respectively. These results suggest that for noisy data,
SVM may perform better than ANN for this problem.
Iris recognition has expanded from controlled settings to uncontrolled settings (on the move, from a distance)
where blur is more likely to be present in the images. More research is needed to quantify the impact of blur on iris
recognition. In this paper we study the effect of out-of-focus blur on iris recognition performance from images
captured with out-of-focus blur produced at acquisition. A key aspect to this study is that we are able to create a
range of blur based on changing focus of the camera during acquisition. We quantify the produced out-of-focus
blur based on the Laplacian of Gaussian operator and compare it to the gold standard of the modulation transfer
function (MTF) of a calibrated black/white chart. The sharpness measure uses an unsegmented iris images from a
video sequence with changing focus and offers a good approximation of the standard MTF. We examined the
effect of the 9 blur levels on iris recognition performance. Our results have shown that for moderately blurry
images (sharpness at least 50%) the drop in performance does not exceed 5% from the baseline (100% sharpness).
Presented in this paper is the environmental testing of Wireless Intelligent Sensor and Actuator Network (WISAN) currently under development at Clarkson University for the use of long-term structural health monitoring of civil infrastructure. The wireless sensor nodes will undergo controlled mechanical vibration and environmental testing in the laboratory. A temperature chamber will be used to perform temperature cycle tests on the sensor nodes. The temperature chamber will also houses a small shaker capable of introducing mechanical loading under the controlled temperature
cycle tests. At low temperatures, the resistance of the electronics processing and storage characteristics will be studied. Also, the testing will look at volume expansion and degradation of characteristics due to freezing, degradation of functions and performance, and mechanical characteristics caused by contraction. At high temperatures, temperature-related changes in sensor nodes due to excessively high temperatures will be investigated. Also studied will be the effects of temperature cycles, including the thermal stresses induced in the nodes and housing and the distortion caused due to expansion and contraction, fatigue, cracks, and changes in electrical characteristics due to mechanical displacement. And finally, mechanical vibration loading will be introduced to the WISAN sensor nodes. Mechanical looseness, fatigue destruction, wire disconnection, damage due to harmonic vibration, defective socket contact, joint
wear, destruction due to harmonics, lead breakage, occurrence of noise and abnormal vibration, cracking will be monitored. The eventual goal of the tests is to verify WISAN's performance under anticipated field conditions in which the sensors will be deployed.
The sensitivity and consistency of a damage index based on instantaneous phase values obtained through vibration measurements of a structure is investigated experimentally. An 'empirical mode decomposition' is performed to decompose structural vibrations into a small number of 'intrinsic mode functions' following the methodology generally known as the Hilbert-Huang Transform. Instantaneous phase information is derived through the Hilbert transform of intrinsic mode functions. The damage index is based on the idea that the difference in phase functions between any two points on a structure is altered if the structure is damaged. Experimental investigations are performed on a beam structure with varying excitations (white noise signals), damage levels, and damage locations. The damage index shows generally consistent results, but its sensitivity to damages needs improvements for practical applications.
This paper presents Wireless Intelligent Sensor and Actuator Network (WISAN) as a scalable wireless platform for
structural health monitoring. Design of WISAN targeted key issues arising in applications of structural health
monitoring. First, scalability of system from a few sensors to hundreds of sensors is provided through hierarchical
cluster-tree network architecture. Special consideration is given to reliable delivery of wireless data in real-world
conditions. Second, a possibility of autonomous operation of sensor nodes from energy harvesters is ensured through
extremely low power consumption in operational and standby modes of operation. Third, all the sensors and actuators
operate in globally synchronized time on the order of a few microseconds through utilization of the beaconing
mechanism of IEEE802.15.4 standard. Fourth, depending on application requirements, the system is capable of
delivering real-time streams of sensor data or performing on-sensor storage and/or processing with result transmission.
Finally, a capability to work with heterogeneous arrays of sensors and actuators is ensured by a variety of analog and
digital interfaces. Results of experimental tests validate the performance of the WISAN.
Life cycle monitoring of civil infrastructure such as bridges and buildings is critical to the long-term operational cost and safety of aging structures. The widespread use of Structural Health Monitoring (SHM) systems is limited due to unavailability of specialized data acquisition equipment, high cost of generic equipment, and absence of fully automatic decision support systems.
The goals of the presented project include: first, design of a Wireless Intelligent Sensor and Actuator Network (WISAN) and creation of an inexpensive set of instrumentation for the tasks of structural health monitoring; second, development of a SHM method, which is suitable for autonomous structural health monitoring.
The design of the wireless sensor network is aimed at applications of structural health monitoring, addressing the issues of achieving a low cost per sensor, higher reliability, sources of energy for the network nodes, energy-efficient distribution of the computational load, security and coexistence in the ISM radio bands. The practical applicability of the sensor network is increased through utilization of computational intelligence and support of signal generation capabilities.
The automated SHM method is based on the method of modal strain energy, though other SHM methods will be supported as well. The automation tasks include automation of the modal identification through ambient vibrations, classification of the acquired mode shapes, and automatic evaluation of the structural health.