Optimal sensor placement within a hybrid dense sensor network using an adaptive genetic algorithm with learning gene pool

This work develops optimal sensor placement within a hybrid dense sensor network used in the construction of accurate strain maps for large-scale structural components. Realization of accurate strain maps is imperative for improved strain-based fault diagnosis and prognosis health management in large-scale structures. Here, an objective function specifically formulated to reduce type I and II errors and an adaptive mutation-based genetic algorithm for the placement of sensors within the hybrid dense sensor network are introduced. The objective function is based on the linear combination method and validates sensor placement while increasing information entropy. Optimal sensor placement is achieved through a genetic algorithm that leverages the concept that not all potential sensor locations contain the same level of information. The level of information in a potential sensor location is taught to subsequent generations through updating the algorithm’s gene pool. The objective function and genetic algorithm are experimentally validated for a cantilever plate under three loading cases. Results demonstrate the capability of the learning gene pool to effectively and repeatedly find a Pareto-optimal solution faster than its non-adaptive gene pool counterpart

Distributed thin film sensor array for damage detection and localization

The authors have developed a capacitive-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. The measurement principle is based on a measurable change in capacitance provoked by strain. In the case of bidirectional in-plane strain, the sensor output contains the additive measurement of both principal strain components. In this paper, we present an algorithm for retrieving unidirectional strain from the bidirectional measurements of the capacitive-based thin film sensor when place in a hybrid dense sensor network with state-of-the-art unidirectional strain sensors. The algorithm leverages the advantages of a hybrid dense network for application of the thin film sensor to reconstruct the surface strain maps. A bidirectional shape function is assumed, and it is differentiated to obtain expressions for planar strain. A least squares estimator (LSE) is used to reconstruct the planar strain map from the networks measurements, after the system’s boundary conditions have been enforced in the model. The coefficients obtained by the LSE can be used to reconstruct the estimated strain map. Results from numerical simulations and experimental investigations show good performance of the algorithm

Algorithm for decomposition of additive strain from dense network of thin film sensors

The authors have developed a capacitive-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. The measurement principle is based on a measurable change in capacitance provoked by strain. In the case of bi-directional in-plane strain, the sensor output contains the additive measurement of both principal strain components. In this paper, we present an algorithm for retrieving the directional strain from measurements. The algorithm leverages the dense network application of the thin film sensor to reconstruct the surface strain map. A bi-directional shape function is assumed, and it is differentiated to obtain expressions for planar strain. A least square estimator (LSE) is used to reconstruct the planar strain map from the sensors measurement’s, after the system’s boundary conditions have been enforced in the model. The coefficients obtained by the LSE can be used to reconstruct the estimated strain map or the deflection shape directly. Results from numerical simulations and experimental investigations show good performance of the algorithm, in particular for monitoring surface strain on cantilever plates

Experimental wind tunnel study of a smart sensing skin for condition evaluation of a wind turbine blade

Condition evaluation of wind turbine blades is difficult due to their large size, complex geometry and lack of economic and scalable sensing technologies capable of detecting, localizing, and quantifying faults over a blade\u27s global area. A solution is to deploy inexpensive large area electronics over strategic areas of the monitored component, analogous to sensing skin. The authors have previously proposed a large area electronic consisting of a soft elastomeric capacitor (SEC). The SEC is highly scalable due to its low cost and ease of fabrication, and can, therefore, be used for monitoring large-scale components. A single SEC is a strain sensor that measures the additive strain over a surface. Recently, its application in a hybrid dense sensor network (HDSN) configuration has been studied, where a network of SECs is augmented with a few off-the-shelf strain gauges to measure boundary conditions and decompose the additive strain to obtain unidirectional surface strain maps. These maps can be analyzed to detect, localize, and quantify faults. In this work, we study the performance of the proposed sensing skin at conducting condition evaluation of a wind turbine blade model in an operational environment. Damage in the form of changing boundary conditions and cuts in the monitored substrate are induced into the blade. An HDSN is deployed onto the interior surface of the substrate, and the blade excited in a wind tunnel. Results demonstrate the capability of the HDSN and associated algorithms to detect, localize, and quantify damage. These results show promise for the future deployment of fully integrated sensing skins deployed inside wind turbine blades for condition evaluation

Damage detection and localization algorithm using a dense sensor network of thin film sensors

The authors have recently proposed a hybrid dense sensor network consisting of a novel, capacitive-based thin-film electronic sensor for monitoring strain on mesosurfaces and fiber Bragg grating sensors for enforcing boundary conditions on the perimeter of the monitored area. The thin-film sensor monitors local strain over a global area through transducing a change in strain into a change in capacitance. In the case of bidirectional in-plane strain, the sensor output contains the additive measurement of both principal strain components. When combined with the mature technology of fiber Bragg grating sensors, the hybrid dense sensor network shows potential for the monitoring of mesoscale systems. In this paper, we present an algorithm for the detection, quantification, and localization of strain within a hybrid dense sensor network. The algorithm leverages the advantages of a hybrid dense sensor network for the monitoring of large scale systems. The thin film sensor is used to monitor strain over a large area while the fiber Bragg grating sensors are used to enforce the uni-directional strain along the perimeter of the hybrid dense sensor network. Orthogonal strain maps are reconstructed by assuming different bidirectional shape functions and are solved using the least squares estimator to reconstruct the planar strain maps within the hybrid dense sensor network. Error between the estimated strain maps and measured strains is extracted to derive damage detecting features, dependent on the selected shape functions. Results from numerical simulations show good performance of the proposed algorithm

Smart bricks for strain sensing and crack detection in masonry structures

The paper proposes the novel concept of smart bricks as a durable sensing solution for structural health monitoring of masonry structures. The term smart bricks denotes piezoresistive clay bricks with suitable electronics capable of outputting measurable changes in their electrical properties under changes in their state of strain. This feature can be exploited to evaluate stress at critical locations inside a masonry wall and to detect changes in loading paths associated with structural damage, for instance following an earthquake. Results from an experimental campaign show that normal clay bricks, fabricated in the laboratory with embedded electrodes made of a special steel for resisting the high baking temperature, exhibit a quite linear and repeatable piezoresistive behavior. That is a change in electrical resistance proportional to a change in axial strain. In order to be able to exploit this feature for strain sensing, high-resolution electronics are used with a biphasic DC measurement approach to eliminate any resistance drift due to material polarization. Then, an enhanced nanocomposite smart brick is proposed, where titania is mixed with clay before baking, in order to enhance the brick\u27s mechanical properties, improve its noise rejection, and increase its electrical conductivity. Titania was selected among other possible conductive nanofillers due to its resistance to high temperatures and its ability to improve the durability of construction materials while maintaining the aesthetic appearance of clay bricks. An application of smart bricks for crack detection in masonry walls is demonstrated by laboratory testing of a small-scale wall specimen under different loading conditions and controlled damage. Overall, it is demonstrated that a few strategically placed smart bricks enable monitoring of the state of strain within the wall and provide information that is capable of crack detection

Experimental damage detection of wind turbine blade using thin film sensor array

Damage detection of wind turbine blades is difficult due to their large sizes and complex geometries. Additionally, economic restraints limit the viability of high-cost monitoring methods. While it is possible to monitor certain global signatures through modal analysis, obtaining useful measurements over a blade\u27s surface using off-the-shelf sensing technologies is difficult and typically not economical. A solution is to deploy dedicated sensor networks fabricated from inexpensive materials and electronics. The authors have recently developed a novel large-area electronic sensor measuring strain over very large surfaces. The sensing system is analogous to a biological skin, where local strain can be monitored over a global area. In this paper, we propose the utilization of a hybrid dense sensor network of soft elastomeric capacitors to detect, localize, and quantify damage, and resistive strain gauges to augment such dense sensor network with high accuracy data at key locations. The proposed hybrid dense sensor network is installed inside a wind turbine blade model and tested in a wind tunnel to simulate an operational environment. Damage in the form of changing boundary conditions is introduced into the monitored section of the blade. Results demonstrate the ability of the hybrid dense sensor network, and associated algorithms, to detect, localize, and quantify damage

Crack detection in RC structural components using a collaborative data fusion approach based on smart concrete and large-area sensors

Recent advances in the fields of nanocomposite technologies have enabled the development of highly scalable, low-cost sensing solution for civil infrastructures. This includes two sensing technologies, recently proposed by the authors, engineered for their high scalability, low-cost and mechanical simplicity. The first sensor consists of a smart-cementitious material doped with multi-wall carbon nanotubes, which has been demonstrated to be suitable for monitoring its own deformations (strain) and damage state (cracks). Integrated to a structure, this smart cementitious material can be used for detecting damage or strain through the monitoring of its electrical properties. The second sensing technology consists of a sensing skin developed from a flexible capacitor that is mounted externally onto the structure. When deployed in a dense sensor network configuration, these large area sensors are capable of covering large surfaces at low cost and can monitor both strain- and crack-induced damages. This work first presents a comparison of the capabilities of both technologies for crack detection in a concrete plate, followed by a fusion of sensor data for increased damage detection performance. Experimental results are conducted on a 50 50 5 cm3 plate fabricated with smart concrete and equipped with a dense sensor network of 20 large area sensors. Results show that both novel technologies are capable of increased damage localization when used concurrently