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Prof. Dr. Fu-Kuo Chang
Structures and Composites Laboratory, Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA

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0 Structural Health Monitoring
0 smart structures
0 multi-functional materials
0 design of integrated structures
0 design and damage tolerance of composites structures

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Structural Health Monitoring
smart structures
multi-functional materials

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Reference work
Published: 22 August 2021 in Handbook of Nondestructive Evaluation 4.0
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Structural health monitoring (SHM) is an emerging technology that provides a high-resolution real-time damage state-sensing awareness and self-diagnostic capabilities enabled by a distributed sensor network. This technology is being used in the Industrial Internet of Things (IIoT) environment (a) to extend the duration of the service life of structural platfroms; (b) to increase their reliability; and (c) to reduce their maintenance and operational cost. This chapter discusses SHM systems, their design, sensor types, and usage through distributed sensing for reliable monitoring in IIoT applications. A distributed network of structurally integrated sensors for SHM diagnostics enables efficient real-time monitoring of entire structures. A carrier layer (SMART layer) is designed in such a way as to eliminate the need for each sensor to be installed individually in a structure. The layer consists of a network of distributed piezoelectric sensors that are individually bonded to it and is manufactured utilizing a standard flex-circuit construction technique in order to connect a large number of sensors. Advanced signal processing, diagnosis methods, and system hardware are implemented for damage monitoring in different complex structures such as rotorcraft, pipeline, paper manufacturing equipment, and high speed train.

ACS Style

M. Faisal Haider; Amrita Kumar; Irene Li; Fu-Kuo Chang. Sensors, Sensor Network, and SHM. Handbook of Nondestructive Evaluation 4.0 2021, 1 -35.

AMA Style

M. Faisal Haider, Amrita Kumar, Irene Li, Fu-Kuo Chang. Sensors, Sensor Network, and SHM. Handbook of Nondestructive Evaluation 4.0. 2021; ():1-35.

Chicago/Turabian Style

M. Faisal Haider; Amrita Kumar; Irene Li; Fu-Kuo Chang. 2021. "Sensors, Sensor Network, and SHM." Handbook of Nondestructive Evaluation 4.0 , no. : 1-35.

Journal article
Published: 16 May 2020 in Sensors
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Tactile sensing is paramount for robots operating in human-centered environments to help in understanding interaction with objects. To enable robots to have sophisticated tactile sensing capability, researchers have developed different kinds of electronic skins for robotic hands and arms in order to realize the ‘sense of touch’. Recently, Stanford Structures and Composites Laboratory developed a robotic electronic skin based on a network of multi-modal micro-sensors. This skin was able to identify temperature profiles and detect arm strikes through embedded sensors. However, sensing for the static pressure load is yet to be investigated. In this work, an electromechanical impedance-based method is proposed to investigate the response of piezoelectric sensors under static normal pressure loads. The smart skin sample was firstly fabricated by embedding a piezoelectric sensor into the soft silicone. Then, a series of static pressure tests to the skin were conducted. Test results showed that the first peak of the real part impedance signal was sensitive to static pressure load, and by using the proposed diagnostic method, this test setup could detect a resolution of 0.5 N force. Numerical simulation methods were then performed to validate the experimental results. The results of the numerical simulation prove the validity of the experiments, as well as the robustness of the proposed method in detecting static pressure loads using the smart skin.

ACS Style

Cheng Liu; Yitao Zhuang; Amir Nasrollahi; Lingling Lu; Mohammad Faisal Haider; Fu-Kuo Chang. Static Tactile Sensing for a Robotic Electronic Skin via an Electromechanical Impedance-Based Approach. Sensors 2020, 20, 2830 .

AMA Style

Cheng Liu, Yitao Zhuang, Amir Nasrollahi, Lingling Lu, Mohammad Faisal Haider, Fu-Kuo Chang. Static Tactile Sensing for a Robotic Electronic Skin via an Electromechanical Impedance-Based Approach. Sensors. 2020; 20 (10):2830.

Chicago/Turabian Style

Cheng Liu; Yitao Zhuang; Amir Nasrollahi; Lingling Lu; Mohammad Faisal Haider; Fu-Kuo Chang. 2020. "Static Tactile Sensing for a Robotic Electronic Skin via an Electromechanical Impedance-Based Approach." Sensors 20, no. 10: 2830.

Journal article
Published: 29 April 2020 in Sensors
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This article presents the development of a stretchable sensor network with high signal-to-noise ratio and measurement accuracy for real-time distributed sensing and remote monitoring. The described sensor network was designed as an island-and-serpentine type network comprising a grid of sensor “islands” connected by interconnecting “serpentines.” A novel high-yield manufacturing process was developed to fabricate networks on recyclable 4-inch wafers at a low cost. The resulting stretched sensor network has 17 distributed and functionalized sensing nodes with low tolerance and high resolution. The sensor network includes Piezoelectric (PZT), Strain Gauge (SG), and Resistive Temperature Detector (RTD) sensors. The design and development of a flexible frame with signal conditioning, data acquisition, and wireless data transmission electronics for the stretchable sensor network are also presented. The primary purpose of the frame subsystem is to convert sensor signals into meaningful data, which are displayed in real-time for an end-user to view and analyze. The challenges and demonstrated successes in developing this new system are demonstrated, including (a) developing separate signal conditioning circuitry and components for all three sensor types (b) enabling simultaneous sampling for PZT sensors for impact detection and (c) configuration of firmware/software for correct system operation. The network was expanded with an in-house developed automated stretch machine to expand it to cover the desired area. The released and stretched network was laminated into an aerospace composite wing with edge-mount electronics for signal conditioning, processing, power, and wireless communication.

ACS Style

Xiyuan Chen; Loic Maxwell; Franklin Li; Amrita Kumar; Elliot Ransom; Tanay Topac; Sera Lee; Mohammad Faisal Haider; Sameh Dardona; Fu-Kuo Chang. Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing. Sensors 2020, 20, 2528 .

AMA Style

Xiyuan Chen, Loic Maxwell, Franklin Li, Amrita Kumar, Elliot Ransom, Tanay Topac, Sera Lee, Mohammad Faisal Haider, Sameh Dardona, Fu-Kuo Chang. Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing. Sensors. 2020; 20 (9):2528.

Chicago/Turabian Style

Xiyuan Chen; Loic Maxwell; Franklin Li; Amrita Kumar; Elliot Ransom; Tanay Topac; Sera Lee; Mohammad Faisal Haider; Sameh Dardona; Fu-Kuo Chang. 2020. "Design and Integration of a Wireless Stretchable Multimodal Sensor Network in a Composite Wing." Sensors 20, no. 9: 2528.

Journal article
Published: 11 January 2019 in Sensors
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The vibration of a wing structure in the air reflects coupled aerodynamic–mechanical responses under varying flight states that are defined by the angle of attack and airspeed. It is of great challenge to identify the flight state from the complex vibration signals. In this paper, a novel one-dimension convolutional neural network (CNN) is developed, which is able to automatically extract useful features from the structural vibration of a recently fabricated self-sensing wing through wind-tunnel experiments. The obtained signals are firstly decomposed into various subsignals with different frequency bands via dual-tree complex-wavelet packet transformation. Then, the reconstructed subsignals are selected to form the best combination for multichannel inputs of the CNN. A swarm-based evolutionary algorithm called grey-wolf optimizer is utilized to optimize a set of key parameters of the CNN, which saves considerable human efforts. Two case studies demonstrate the high identification accuracy and robustness of the proposed method over standard deep-learning methods in flight-state identification, thus providing new perspectives in self-awareness toward the next generation of intelligent air vehicles.

ACS Style

Xi Chen; Fotis Kopsaftopoulos; Qi Wu; He Ren; Fu-Kuo Chang. A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification. Sensors 2019, 19, 275 .

AMA Style

Xi Chen, Fotis Kopsaftopoulos, Qi Wu, He Ren, Fu-Kuo Chang. A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification. Sensors. 2019; 19 (2):275.

Chicago/Turabian Style

Xi Chen; Fotis Kopsaftopoulos; Qi Wu; He Ren; Fu-Kuo Chang. 2019. "A Self-Adaptive 1D Convolutional Neural Network for Flight-State Identification." Sensors 19, no. 2: 275.

Chapter
Published: 14 November 2018 in Handbook of Dynamic Data Driven Applications Systems
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A novel Data-driven State Awareness (DSA) framework is introduced for the next generation of intelligent “fly-by-feel” aerospace vehicles. The proposed framework is based on two entities: (i) bio-inspired networks of micro-sensors that can provide real-time information on the dynamic aeroelastic response of the structure and (ii) a stochastic “global” identification approach for representing the system dynamics under varying flight states and uncertainty. The evaluation and assessment of the proposed DSA framework is based on a prototype bio-inspired self-sensing intelligent composite wing subjected to a series of wind tunnel experiments under multiple flight states. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, are embedded in the layup of the composite wing in order to provide the sensing capabilities. A novel data-driven stochastic “global” identification approach based on functionally pooled time series models and statistical parameter estimation techniques is employed in order to accurately interpret the sensing data and extract information on the wing aeroelastic behavior and dynamics. The methods’s cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow for the analytical inclusion of both airspeed and angle of attack (AoA) in the model parameters and, hence, system dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight states, each defined by a distinct pair of airspeed and AoA. The obtained results demonstrate the high achievable accuracy and effectiveness of the proposed state-awareness framework, thus opening new perspectives for enabling the next generation of “fly-by-feel” aerospace vehicles.

ACS Style

Fotis Kopsaftopoulos; Fu-Kuo Chang. A Dynamic Data-Driven Stochastic State-Awareness Framework for the Next Generation of Bio-inspired Fly-by-Feel Aerospace Vehicles. Handbook of Dynamic Data Driven Applications Systems 2018, 697 -721.

AMA Style

Fotis Kopsaftopoulos, Fu-Kuo Chang. A Dynamic Data-Driven Stochastic State-Awareness Framework for the Next Generation of Bio-inspired Fly-by-Feel Aerospace Vehicles. Handbook of Dynamic Data Driven Applications Systems. 2018; ():697-721.

Chicago/Turabian Style

Fotis Kopsaftopoulos; Fu-Kuo Chang. 2018. "A Dynamic Data-Driven Stochastic State-Awareness Framework for the Next Generation of Bio-inspired Fly-by-Feel Aerospace Vehicles." Handbook of Dynamic Data Driven Applications Systems , no. : 697-721.

Preprint
Published: 02 October 2018
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This work proposes and analyzes a structurally-integrated lithium-ion battery concept. The multifunctional energy storage composite (MESC) structures developed here encapsulate lithium-ion battery materials inside high-strength carbon-fiber composites and use interlocking polymer rivets to stabilize the electrode layer stack mechanically. These rivets enable load transfer between battery layers, allowing them to store electrical energy while also contributing to the structural load carrying performance, without any modifications to the battery chemistry. The design rationale, fabrication processes, and experimental mechano-electrical characterization of first-generation MESCs are discussed. Experimental results indicate that the MESCs offer electrochemically equivalent performance to the baseline chemistry, despite the disruptive design change. The mechanically-functionalized battery stack’s contribution is assessed via quasi-static three-point bending tests, with results showing significantly improved mechanical stiffness and strength over traditional pouch cells. The rivets minimize interlayer shear movement of the electrode stack, thus allowing it to maintain electrochemical functionalities while carrying mechanical bending. While minimal load application can cause permanent deformation of pouch cells, MESCs maintain their structural integrity and energy-storage capabilities after realistic repeated loading. The results obtained demonstrate the mechanical robustness of MESCs, which allows them to be fabricated as energy-storing structures for electric vehicles and other applications.

ACS Style

Purim Ladpli; Raphael Nardari; Fotis Kopsaftopoulos; Fu-Kuo Chang. Multifunctional Energy Storage Composite Structures with Embedded Lithium-ion Batteries. 2018, 1 .

AMA Style

Purim Ladpli, Raphael Nardari, Fotis Kopsaftopoulos, Fu-Kuo Chang. Multifunctional Energy Storage Composite Structures with Embedded Lithium-ion Batteries. . 2018; ():1.

Chicago/Turabian Style

Purim Ladpli; Raphael Nardari; Fotis Kopsaftopoulos; Fu-Kuo Chang. 2018. "Multifunctional Energy Storage Composite Structures with Embedded Lithium-ion Batteries." , no. : 1.

Journal article
Published: 28 September 2018 in Sensors
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Smart structures mimic biological systems by using thousands of sensors serving as a nervous system analog. One approach to give structures this sensing ability is to develop a multifunctional sensor network. Previous work has demonstrated stretchable sensor networks consisting of temperature sensors and impact detectors for monitoring external environments and interacting with other objects. The objective of this work is to develop distributed, robust and reliable strain gauges for obtaining the strain distribution of a designated region on the target structure. Here, we report a stretchable network that has 27 rosette strain gauges, 6 resistive temperature devices and 8 piezoelectric transducers symmetrically distributed over an area of 150 × 150 mm to map and quantify multiple physical stimuli with a spatial resolution of 2.5 × 2.5 mm. We performed computational modeling of the network stretching process to improve measurement accuracy and conducted experimental characterizations of the microfabricated strain gauges to verify their gauge factor and temperature coefficient. Collectively, the results represent a robust and reliable sensing system that is able to generate a distributed strain profile of a common structure. The reported strain gauge network may find a wide range of applications in morphing wings, smart buildings, autonomous cars and intelligent robots.

ACS Style

Xiyuan Chen; Tanay Topac; Wyatt Smith; Purim Ladpli; Cheng Liu; Fu-Kuo Chang. Characterization of Distributed Microfabricated Strain Gauges on Stretchable Sensor Networks for Structural Applications. Sensors 2018, 18, 3260 .

AMA Style

Xiyuan Chen, Tanay Topac, Wyatt Smith, Purim Ladpli, Cheng Liu, Fu-Kuo Chang. Characterization of Distributed Microfabricated Strain Gauges on Stretchable Sensor Networks for Structural Applications. Sensors. 2018; 18 (10):3260.

Chicago/Turabian Style

Xiyuan Chen; Tanay Topac; Wyatt Smith; Purim Ladpli; Cheng Liu; Fu-Kuo Chang. 2018. "Characterization of Distributed Microfabricated Strain Gauges on Stretchable Sensor Networks for Structural Applications." Sensors 18, no. 10: 3260.

Journal article
Published: 29 June 2018 in IEEE Robotics and Automation Letters
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Active sensing provides a way to assess whether a thin film of gecko-inspired adhesive has made good contact with a surface. This knowledge is useful for applications like gripping objects in space where a failed grasp could lead to loss of the object. Our active sensing approach uses Lamb waves in thin bilayers, excited, and detected by piezoelectric strips. From the theory, we describe how attenuation increases with contact boundary condition changes. We validated the theory using frustrated total internal reflection imaging, showing that attenuation increases as the contact area grows. Pull tests on different textures of acrylic plate show that the slope change of the signal can predict the maximum adhesion limit with a 10 N window and predict impending failure with a detection rate >80%. Lifting a cylindrical object shows that the sensor can signal different types of failures with a detection rate >85%, associated with unstable grasping.

ACS Style

Tae Myung Huh; Cheng Liu; Jiro Hashizume; Tony G. Chen; Srinivasan A. Suresh; Fu-Kuo Chang; Mark R. Cutkosky. Active Sensing for Measuring Contact of Thin Film Gecko-Inspired Adhesives. IEEE Robotics and Automation Letters 2018, 3, 3263 -3270.

AMA Style

Tae Myung Huh, Cheng Liu, Jiro Hashizume, Tony G. Chen, Srinivasan A. Suresh, Fu-Kuo Chang, Mark R. Cutkosky. Active Sensing for Measuring Contact of Thin Film Gecko-Inspired Adhesives. IEEE Robotics and Automation Letters. 2018; 3 (4):3263-3270.

Chicago/Turabian Style

Tae Myung Huh; Cheng Liu; Jiro Hashizume; Tony G. Chen; Srinivasan A. Suresh; Fu-Kuo Chang; Mark R. Cutkosky. 2018. "Active Sensing for Measuring Contact of Thin Film Gecko-Inspired Adhesives." IEEE Robotics and Automation Letters 3, no. 4: 3263-3270.

Journal article
Published: 29 April 2018 in Sensors
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In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.

ACS Style

Xi Chen; Fotis Kopsaftopoulos; Qi Wu; He Ren; Fu-Kuo Chang. Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches. Sensors 2018, 18, 1379 .

AMA Style

Xi Chen, Fotis Kopsaftopoulos, Qi Wu, He Ren, Fu-Kuo Chang. Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches. Sensors. 2018; 18 (5):1379.

Chicago/Turabian Style

Xi Chen; Fotis Kopsaftopoulos; Qi Wu; He Ren; Fu-Kuo Chang. 2018. "Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches." Sensors 18, no. 5: 1379.

Research article
Published: 28 October 2017 in Structural Health Monitoring
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Monitoring the bondline integrity of adhesively bonded joints is one of the most critical concerns in the design of aircraft structures to date. Due to the lack of confidence on the integrity of the bondline both during fabrication and service, the industry standards and regulations require assembling the primary airframe structure using the inefficient “black-aluminum” approach, that is, drill holes and use fasteners. Furthermore, state-of-the-art non-destructive evaluation and structural health monitoring approaches are not yet able to provide mature solutions on the issue of bondline integrity monitoring. Therefore, the objective of this work is the introduction and feasibility investigation of a novel bondline integrity monitoring method that is based on the use of piezoelectric sensors embedded inside adhesively bonded joints in order to provide an early detection of bondline degradation. The proposed approach incorporates (1) micro-sensors embedded inside the adhesive layer leaving a minimal footprint on the material, (2) numerical and analytical modeling of the electromechanical impedance of the adhesive bondline, and (3) electromechanical impedance–based diagnostic algorithms for monitoring and assessing the bondline integrity. The experimental validation and assessment of the proposed approach is achieved via the design and fabrication of prototype adhesively bonded lap joints with embedded piezoelectric sensors and a series of mechanical tests under various static and dynamic (fatigue) loading conditions. The obtained results demonstrate the potential of the proposed approach in providing increased confidence on the use of adhesively bonded joints for aerospace structures.

ACS Style

Yitao Zhuang; Fotis Kopsaftopoulos; Roberto Dugnani; Fu-Kuo Chang. Integrity monitoring of adhesively bonded joints via an electromechanical impedance-based approach. Structural Health Monitoring 2017, 17, 1031 -1045.

AMA Style

Yitao Zhuang, Fotis Kopsaftopoulos, Roberto Dugnani, Fu-Kuo Chang. Integrity monitoring of adhesively bonded joints via an electromechanical impedance-based approach. Structural Health Monitoring. 2017; 17 (5):1031-1045.

Chicago/Turabian Style

Yitao Zhuang; Fotis Kopsaftopoulos; Roberto Dugnani; Fu-Kuo Chang. 2017. "Integrity monitoring of adhesively bonded joints via an electromechanical impedance-based approach." Structural Health Monitoring 17, no. 5: 1031-1045.

Proceedings article
Published: 28 September 2017 in Structural Health Monitoring 2017
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This work presents a novel scalable and field deployable framework for estimating battery state of charge (SoC) based on pitch-catch guided wave propagation using lowprofile built-in piezoelectric transducers. A preliminary study is performed through experiments using piezoelectric disc transducers mounted on commercial lithium-ion (Li-ion) pouch batteries. Similar experiments are carried out on Multifunctional Energy Storage (MES) Composites, which are an energy-storing structural material recently developed by the authors. In this work, special emphasis is given to the numerical validation of the variation in guided wave time of flight (ToF) due to the SoC-induced changes in electrode mechanical properties. The experimental results are accurately captured in the numerical simulation, which provides significant insight on the complex coupling between electrochemistry and mechanics. Moreover, a first-pass statistical analysis is conducted which demonstrates the efficacy of using guided wave data to obtain accurate prediction of battery SoC. The study also reveals a promising opportunity for exploiting the feature-rich multi-path nature of guided wave propagation to further improve SoC prediction accuracy.

ACS Style

Purim Ladpli; Fotis Kopsaftopoulos; Fu-Kuo Chang. Battery State of Charge Estimation using Guided Waves— Numerical Validation and Statistical Analysis. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Purim Ladpli, Fotis Kopsaftopoulos, Fu-Kuo Chang. Battery State of Charge Estimation using Guided Waves— Numerical Validation and Statistical Analysis. Structural Health Monitoring 2017. 2017; ():1.

Chicago/Turabian Style

Purim Ladpli; Fotis Kopsaftopoulos; Fu-Kuo Chang. 2017. "Battery State of Charge Estimation using Guided Waves— Numerical Validation and Statistical Analysis." Structural Health Monitoring 2017 , no. : 1.

Proceedings article
Published: 28 September 2017 in Structural Health Monitoring 2017
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Full life-cycle and real-time structural health monitoring (SHM) rely on numerous, heterogeneous sets of data collected from sensors and extracting feathers that support the estimation of the health condition of a bridge. It is the principal challenges facing the SHM application on transmission and storing such huge amounts of data. Compressive sensing (CS) is a novel data acquisition method whereby the compression is done in a sensor simultaneously with the sampling. In this work, we established the possibility of compressed sensing to address this challenges on bridge SHM. Based on the sparsity of the deformation data of bridge, we proposed a random sampling scheme based on CS to minimize the number of field data. The CS recovery performance is mostly determined by the decomposition basis which is associated with the sparsity of the sampling signal. Different bases have been tested to recover the deformation data. Experimental results demonstrate the proposed method allows a reduction of the measurement data with an acceptable recovery accuracy. And reconstruction performance based on DWT to sparse transform is better than that based on DCT to sparse transform. When the compression ratio is above 0.6, the reconstruction error grows moderately; whereas the reconstruction error grows rapidly as the compression ratio is below 0.6. With the compression ratio decreased from 0.6 to 0.2, reconstruction error is reduced about 2.5 times by using DWT and reduced about 2 times by using DCT.

ACS Style

Hailin Cao; Yinli Tian; Jianmei Lei; Xiaoheng Tan; Dongyue Gao; Fotis Kopsaftopoulos; Fu-Kuo Chang. Deformation Data Recovery Based on Compressed Sensing in Bridge Structural Health Monitoring. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Hailin Cao, Yinli Tian, Jianmei Lei, Xiaoheng Tan, Dongyue Gao, Fotis Kopsaftopoulos, Fu-Kuo Chang. Deformation Data Recovery Based on Compressed Sensing in Bridge Structural Health Monitoring. Structural Health Monitoring 2017. 2017; ():1.

Chicago/Turabian Style

Hailin Cao; Yinli Tian; Jianmei Lei; Xiaoheng Tan; Dongyue Gao; Fotis Kopsaftopoulos; Fu-Kuo Chang. 2017. "Deformation Data Recovery Based on Compressed Sensing in Bridge Structural Health Monitoring." Structural Health Monitoring 2017 , no. : 1.

Proceedings article
Published: 28 September 2017 in Structural Health Monitoring 2017
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Smart structures revolutionize the traditional manned systems and challenge the structural health monitoring (SHM) field from a completely new perspective. Advanced sensing technology enables highly intelligent systems by embedding sensor networks into composite materials for smart structures such as fly-by-feel flights and intelligent robots. In this paper, we present a highly stretchable network with multifunctional sensor nodes microfabricated on a flexible polymer substrate. For the first time, we are capable of integrating three types of sensors with their appropriate materials into one single network. 6 platinum resistance temperature detectors (RTDs), 27 constantan strain gauges (SGs) and 8 lead zirconate titanate piezoelectric transducers (PZTs) with their unique patterns are distributed over the entire network. Finite element modeling has been performed to analyze the stress distribution along the node-wire connections during the stretching process in order to minimize nodal rotations. By taking advantage of a serpentine wire design, the network can be expanded 700% in area while maintaining high yield and low variation. A series of sensor characterizations have been conducted to validate the functionality of RTDs, SGs and PZTs. It is worth mentioning that the sensitivity of the RTD is 0.1 V/°C and the minimum detectable strain of the SG is as low as 2.26 με. The multifunctional sensor network will be embedded into a composite wing to provide real-time aeroelastic feedbacks in a wind tunnel

ACS Style

Xiyuan Chen; Tanay Topac; Wyatt Smith; Purim Ladpli; Hailin Cao; Fu-Kuo Chang. Development of a Multifunctional Stretchable Sensor Network for Smart Structures. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Xiyuan Chen, Tanay Topac, Wyatt Smith, Purim Ladpli, Hailin Cao, Fu-Kuo Chang. Development of a Multifunctional Stretchable Sensor Network for Smart Structures. Structural Health Monitoring 2017. 2017; ():1.

Chicago/Turabian Style

Xiyuan Chen; Tanay Topac; Wyatt Smith; Purim Ladpli; Hailin Cao; Fu-Kuo Chang. 2017. "Development of a Multifunctional Stretchable Sensor Network for Smart Structures." Structural Health Monitoring 2017 , no. : 1.

Proceedings article
Published: 28 September 2017 in Structural Health Monitoring 2017
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Assessing the reliability of damage quantification is a critical and necessary process for the evaluation of Nondestructive Evaluation (NDE) or Structural Health Monitoring (SHM) techniques. When it comes to NDE techniques, appropriate processes have been matured and established for asserting the reliability of damage quantification. However, such techniques offer solutions which that can be applied offline, are time consuming, and oftentimes quite expensive. On the other hand, for the case of SHM-based methods, and although several probabilistic methods have been proposed in the state-of-the-art literature, the task of damage quantification of active sensing techniques poses significant challenges that need to be properly addressed. Specifically, a major safety concern in aerospace structures is related to fatigue induced cracks for which accurate and reliable quantification is a critical issue for achieving the design performance and ensuring the aircraft safety. The main challenges associated with the quantification of fatigue cracks originate from the acousto-ultrasonic response discrepancies in similar structural materials that are due to variations in operating and environmental conditions, sensor positioning, damage characteristics (crack location, orientation, and propagation), and the effectiveness of the employed diagnostic algorithms. Recent studies have shown that apart from the operating/environmental conditions, the main sources of variation and uncertainty in the acoustic response are related to the location of the sensors and the damage location and propagation patterns. Trying to address the latter, this study presents an active sensing damage quantification approach that is based on the use of multiple piezoelectric sensors and corresponding acousto-ultrasonic damage propagation paths. Multiple paths from a multiple-sensor configuration unit, referred to as unit-cell, are used along with an adaptive weighted averaging method to mitigate the effects of sensor positioning errors and/or uncertainties associated with crack size and orientation. Several coupon-level experiments have been conducted to validate the performance of the method and investigate the convergence of the accuracy for increasing number of sensors.

ACS Style

Susheel Kumar Yadav; Howard Chung; Fotis Kopsaftopoulos; Fu-Kuo Chang. Damage Quantification of Active Sensing Acousto-ultrasound-based SHM Based on a Multi-path Unit-cell Approach. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Susheel Kumar Yadav, Howard Chung, Fotis Kopsaftopoulos, Fu-Kuo Chang. Damage Quantification of Active Sensing Acousto-ultrasound-based SHM Based on a Multi-path Unit-cell Approach. Structural Health Monitoring 2017. 2017; ():1.

Chicago/Turabian Style

Susheel Kumar Yadav; Howard Chung; Fotis Kopsaftopoulos; Fu-Kuo Chang. 2017. "Damage Quantification of Active Sensing Acousto-ultrasound-based SHM Based on a Multi-path Unit-cell Approach." Structural Health Monitoring 2017 , no. : 1.

Proceedings article
Published: 28 September 2017 in Structural Health Monitoring 2017
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Recent work on multifunctional materials has demonstrated that high-strength composites could be integrated with active Li-ion battery material to create high strength and high energy density storage structures that could meet the transportation requirements on mobility and energy storage density. However, the performance and the design of the multifunctional materials require fundamental understanding of the mechanical behavior of the integrated Multifunctional Energy Storage (MES) Composites systems under various loading conditions. Characterization of this new class of multifunctional materials would become very challenging without an adequate simulation model to guide the tests and validate the results. Therefore, this work presents the mechanical simulation and design of the MES Composites system that consists of multiple thin battery layers, polymer reinforcements, and carbon fiber composites, which results significant challenges in simulation and modeling. To tackle these issues, homogenization techniques were adopted to characterize the multi-layer properties of battery material with physics-based constitutive equations combined with non-linear deformation theories to handle the interface between the battery layers. Second, both mechanical and electrical damage and failure modes among battery materials, polymer reinforcements and carbon fiberpolymer interfaces were characterized through appropriate models and experiments. The model of MES Composite has been implemented in a commercial finite element code. A comparison of structural response and failure modes from numerical simulations and experimental tests will be presented in the paper. The simulated strain distribution and its application to Structural Health Monitoring (SHM) on MES Composites will also be discussed. The results of the study showed that the predictions of elastic and damage responses of MES Composites at various loading condition agreed with the test data. With appropriate material parameters determined from experiments, this multi-physics model can be used as a necessary tool to characterize a failure envelop that governs the design of MES Composites under specified electrical and mechanical loads.

ACS Style

Yinan Wang; Anthony Bombik; Purim Ladpli; Fotis Kopsaftopoulos; Fu-Kuo Chang. Numerical Model for Characterization of Multifunctional Energy Storage Composite Cells, Modules, and Systems. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Yinan Wang, Anthony Bombik, Purim Ladpli, Fotis Kopsaftopoulos, Fu-Kuo Chang. Numerical Model for Characterization of Multifunctional Energy Storage Composite Cells, Modules, and Systems. Structural Health Monitoring 2017. 2017; ():1.

Chicago/Turabian Style

Yinan Wang; Anthony Bombik; Purim Ladpli; Fotis Kopsaftopoulos; Fu-Kuo Chang. 2017. "Numerical Model for Characterization of Multifunctional Energy Storage Composite Cells, Modules, and Systems." Structural Health Monitoring 2017 , no. : 1.

Conference paper
Published: 28 September 2017 in Structural Health Monitoring 2017
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With the development of micro-fabrication techniques, multi-functional sensor networks have been created with many micro-sensor nodes for various functions. An UAV wing is integrated with a stretchable sensor network including piezoelectric transducer, strain gauge and resistance temperature detector embedded near the wing surface and this novel design intends to equip the wing with the self-sensing capability similar to bird feathers. One of the main challenges of the state-of-the-art research is how to make the wing aware its flight states in real-time through large amount of sensing signals. Features are extracted and further selected for neural network modelling. Both classic neural network and deep belief network are proposed respectively to map the relationship from the sensing data to physical flight states and compare the identification accuracy with each other. The simulation results show a relatively high identification accuracy of the proposed methods, enabling new perspectives in developing intelligent self-awareness capabilities for next generation smart wings.

ACS Style

Xi Chen; Fotis Kopsaftopoulos; Hailin Cao; Fu-Kuo Chang. Intelligent Flight State Identification of a Self-Sensing Wing through Neural Network Modelling. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Xi Chen, Fotis Kopsaftopoulos, Hailin Cao, Fu-Kuo Chang. Intelligent Flight State Identification of a Self-Sensing Wing through Neural Network Modelling. Structural Health Monitoring 2017. 2017; (shm):1.

Chicago/Turabian Style

Xi Chen; Fotis Kopsaftopoulos; Hailin Cao; Fu-Kuo Chang. 2017. "Intelligent Flight State Identification of a Self-Sensing Wing through Neural Network Modelling." Structural Health Monitoring 2017 , no. shm: 1.

Conference paper
Published: 28 September 2017 in Structural Health Monitoring 2017
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In this work, an experimental study of a novel data-driven fly-by-feel state awareness method is presented. A non-parametric probabilistic approach for stall detection is investigated and assessed via a series of wind tunnel experiments. The method is based on the statistical analysis of the recorded signals and subsequent statistical hypothesis testing and decision making procedures. In this proof-of-concept experimental study, the flight state is defined by two variables, the airspeed and angle of attack. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors, in the form of stretchable sensor networks, are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the development and assessment of a non-parametric fly-by-feel stall detection approach within a probabilistic framework. In this study, special emphasis is given to the early detection of aerodynamic stall without making use of any conventional information related to the attitude of the vehicle. The method is able to provide in real time the probability of stall for an indicative flight scenario that was implemented in the wind tunnel. The obtained results demonstrate the effectiveness and potential of the developed approach

ACS Style

Fotis Kopsaftopoulos; Raphael Nardari; Yu-Hung Li; Fu-Kuo Chang. Data-driven State Awareness for Fly-by-feel Aerial Vehicles: Experimental Assessment of a Non-parametric Probabilistic Stall Detection Approach. Structural Health Monitoring 2017 2017, 1 .

AMA Style

Fotis Kopsaftopoulos, Raphael Nardari, Yu-Hung Li, Fu-Kuo Chang. Data-driven State Awareness for Fly-by-feel Aerial Vehicles: Experimental Assessment of a Non-parametric Probabilistic Stall Detection Approach. Structural Health Monitoring 2017. 2017; (shm):1.

Chicago/Turabian Style

Fotis Kopsaftopoulos; Raphael Nardari; Yu-Hung Li; Fu-Kuo Chang. 2017. "Data-driven State Awareness for Fly-by-feel Aerial Vehicles: Experimental Assessment of a Non-parametric Probabilistic Stall Detection Approach." Structural Health Monitoring 2017 , no. shm: 1.

Research article
Published: 26 May 2017 in Structural Health Monitoring
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An investigation was performed to develop appropriate techniques to design and fabricate (using complementary metal-oxide semiconductor/micro-electro-mechanical systems technologies) highly stretchable networks of distributed sensors and organic diodes that could be stretched, and surface-mounted or embedded into polymeric materials to cover an area several orders of magnitude larger than its original size. Both analysis and experiments were performed to validate the design and fabrication methods. The techniques sought to reduce stresses due to network expansion, and a new spin-coated fabrication process was developed to enable high-resolution features in the network. Networks with temperature sensors and piezoelectric sensors were fabricated and tested to demonstrate functionality in advanced composite materials that are common in aircraft.

ACS Style

Zhiqiang Guo; Kyunglok Kim; Nathan Salowitz; Giulia Lanzara; Yinan Wang; Peter Peumans; Fu-Kuo Chang. Functionalization of stretchable networks with sensors and switches for composite materials. Structural Health Monitoring 2017, 17, 598 -623.

AMA Style

Zhiqiang Guo, Kyunglok Kim, Nathan Salowitz, Giulia Lanzara, Yinan Wang, Peter Peumans, Fu-Kuo Chang. Functionalization of stretchable networks with sensors and switches for composite materials. Structural Health Monitoring. 2017; 17 (3):598-623.

Chicago/Turabian Style

Zhiqiang Guo; Kyunglok Kim; Nathan Salowitz; Giulia Lanzara; Yinan Wang; Peter Peumans; Fu-Kuo Chang. 2017. "Functionalization of stretchable networks with sensors and switches for composite materials." Structural Health Monitoring 17, no. 3: 598-623.

Conference paper
Published: 19 April 2017 in Smart Materials and Nondestructive Evaluation for Energy Systems 2017
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ACS Style

Purim Ladpli; Fotis Kopsaftopoulos; Raphael Nardari; Fu-Kuo Chang. Battery charge and health state monitoring via ultrasonic guided-wave-based methods using built-in piezoelectric transducers. Smart Materials and Nondestructive Evaluation for Energy Systems 2017 2017, 1017108 -1017108-12.

AMA Style

Purim Ladpli, Fotis Kopsaftopoulos, Raphael Nardari, Fu-Kuo Chang. Battery charge and health state monitoring via ultrasonic guided-wave-based methods using built-in piezoelectric transducers. Smart Materials and Nondestructive Evaluation for Energy Systems 2017. 2017; ():1017108-1017108-12.

Chicago/Turabian Style

Purim Ladpli; Fotis Kopsaftopoulos; Raphael Nardari; Fu-Kuo Chang. 2017. "Battery charge and health state monitoring via ultrasonic guided-wave-based methods using built-in piezoelectric transducers." Smart Materials and Nondestructive Evaluation for Energy Systems 2017 , no. : 1017108-1017108-12.

Research article
Published: 02 October 2016 in Journal of Intelligent Material Systems and Structures
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Piezoelectric transducers have applications from ultrasonic structural health monitoring to micro-electromechanical systems. Small physical size coupled with large actuation is desirable in many applications, requiring unique transducer designs to take advantage of the material properties. Screen-printed piezoceramics were developed as a means of mass producing mezzo-scale transducers that are geometrically small and light weight, but large enough to generate significant actuation. Screen-printed piezoceramic transducers display significantly different properties than chemically identical bulk ceramic elements, largely attributed to high void fraction of screen-printed piezoceramic materials and detrimental to the functionality of traditional transducer designs. This article presents analysis, simulation, and initial testing of new designs for screen-printed piezoceramic transducers with concentric through-thickness electrodes. Analytical models were developed enabling analysis across material properties and design parameters. Analytical results were verified against finite element models for some designs. Prototypes were created and underwent initial testing to assess the properties of the design.

ACS Style

Nathan Picchietti Salowitz; Sang-Jong Kim; Fotis Kopsaftopoulos; Yu-Hung Li; Fu-Kuo Chang. Design and analysis of radially polarized screen-printed piezoelectric transducers. Journal of Intelligent Material Systems and Structures 2016, 28, 934 -946.

AMA Style

Nathan Picchietti Salowitz, Sang-Jong Kim, Fotis Kopsaftopoulos, Yu-Hung Li, Fu-Kuo Chang. Design and analysis of radially polarized screen-printed piezoelectric transducers. Journal of Intelligent Material Systems and Structures. 2016; 28 (7):934-946.

Chicago/Turabian Style

Nathan Picchietti Salowitz; Sang-Jong Kim; Fotis Kopsaftopoulos; Yu-Hung Li; Fu-Kuo Chang. 2016. "Design and analysis of radially polarized screen-printed piezoelectric transducers." Journal of Intelligent Material Systems and Structures 28, no. 7: 934-946.