Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operationposes a significant threat to the safety of both life and property. Consequently, it becomes imperativ...Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operationposes a significant threat to the safety of both life and property. Consequently, it becomes imperative to conductdamage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types ofpotential damage, and the presence of similar vibration data in adjacent locations make it challenging to achievesatisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmentalnoise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and antinoisecapabilities of bleacher damage diagnosis, this paper proposes improvements to the existing ConvolutionalNeural Network with Training Interference (TICNN). The result is an advanced Convolutional Neural Networkmodel with superior accuracy and robust anti-noise capabilities, referred to as Enhanced TICNN (ETICNN).ETICNN autonomously extracts optimal damage-sensitive features from the original vibration data. To validatethe superiority of the proposed ETICNN, experiments are conducted using the bleacher model from Qatar Universityas the subject. Comparative studies under identical experimental conditions involve TICNN, Deep ConvolutionalNeural Networks with wide first-layer kernels (WDCNN), and One-Dimensional ConvolutionalNeural Network (1DCNN). The experimental findings demonstrate that the ETICNN model achieves the highestaccuracy, approximately 99%, and exhibits robust classification abilities in both Phases I and II of the damagediagnosis experiments. Simultaneously, the ETICNN model demonstrates strong anti-noise capabilities, outperformingTICNN by 3% to 4% and surpassing other models in performance.展开更多
Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the dam...Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.展开更多
In the exploitation of ocean oil and gas, many offshore structures may be damaged due to the severe environment, so an effective method of diagnosing structural damage is urgently needed to locate the damage and evalu...In the exploitation of ocean oil and gas, many offshore structures may be damaged due to the severe environment, so an effective method of diagnosing structural damage is urgently needed to locate the damage and evaluate its severity. Genetic algorithms have become some of the most important global optimization tools and been widely used in many fields in recent years because of their simple operation and strong robustness. Based on the natural frequencies and mode shapes of the structure, the damage diagnosis of a jacket offshore platform is attributed to an optimization problem and studied by using a genetic algorithm. According to the principle that the structural stiffness of a certain direction can be greatly affected only when the brace bar in the corresponding direction is damaged, an improved objective function was proposed in this paper targeting measurement noise and the characteristics of modal identification for offshore platforms. This function can be used as fitness function of a genetic algorithm, and both numerical simulation and physical model test results show that the improved method may locate the structural damage and evaluate the severity of a jacket offshore platform satisfactorily while improving the robustness of evolutionary searching and the reliability of damage diagnosis.展开更多
The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduce...The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.展开更多
An improved CHC algorithm is proposed in the paper and it could be used for the damage diagnosis of structures. It breaks the bottle neck of genetic algorithm in the damage diagnosis of large structures and takes a sh...An improved CHC algorithm is proposed in the paper and it could be used for the damage diagnosis of structures. It breaks the bottle neck of genetic algorithm in the damage diagnosis of large structures and takes a shorter time than the SGA (Stan- dard Genetic Algorithm) in diagnosing structural damage with the same level of error. The case studies show that the algorithm is rapid in convergence and produces satisfactory results in diagnosing both fixed-end beams and jacket offshore platforms.展开更多
Mitochondrial damage is closely related to the occurrence of many diseases.However,accurate monitoring and reporting of mitochondrial damage are not easy.Here,we developed a small molecule fluorescent probe named CB-C...Mitochondrial damage is closely related to the occurrence of many diseases.However,accurate monitoring and reporting of mitochondrial damage are not easy.Here,we developed a small molecule fluorescent probe named CB-Cl,which has splendid spectral properties(large Stokes shift,strong affinity for RNA,etc.)and excellent targeting ability to intracellular mitochondria.After mitochondria were damaged by external stimuli,CB-Cl would light up the nucleolus as a signal reporter.The cascade imaging of mitochondria and nucleolus using CB-Cl can monitor and visualize the mitochondrial status in living cells in real-time.Based on the above advantages,the probe CB-Cl has reference significance for the related research of mitochondrial damage and the prevention and treatment of related diseases.展开更多
Structural health monitoring-based quantitative damage diagnosis technique plays a key role in real-time condition monitoring.Among the current research,piezoelectric(PZT)sensor and Guided Wave(GW)based damage quantif...Structural health monitoring-based quantitative damage diagnosis technique plays a key role in real-time condition monitoring.Among the current research,piezoelectric(PZT)sensor and Guided Wave(GW)based damage quantification methods are promising,which normally establish a calibration model between GW feature and damage degree by experiments on batch specimens,and then conduct the calibration model on the monitored specimen.However,the accuracy of PZT and GW based damage quantification is affected by dispersion introduced by sensor network performance,structural material,and damage propagation among the adopted batch specimens.For improving the accuracy of damage quantification,this paper adopts PZT layer as sensor network and creatively implements theoretical and experimental research on batch PZT layers consistency control.On one hand,a two-level consistency control method based on multidimensional features-Euclidean distance is proposed to ensure the performance consistency of PZT layers placed on different specimens.On the other hand,experimental research on typical aircraft lug structures is also carried out to evaluate the requirement on performance consistency of PZT layers when performing quantitative damage diagnosis,and further verify the proposed two-level consistency control method.Experimental results show that the accuracy of damage quantification raises by 38% when the dispersion of different PZT layers is controlled within 5%.展开更多
In-service structural health monitoring（SHM） technologies are critical for the utilization of composite aircraft structures. We developed a Lamb wave-based in-service SHM technology using built-in piezoelectric actu...In-service structural health monitoring（SHM） technologies are critical for the utilization of composite aircraft structures. We developed a Lamb wave-based in-service SHM technology using built-in piezoelectric actuator/sensor networks to monitor delamination extension in a full-scale composite horizontal tail. The in-service SHM technology combine of damage rapid monitoring（DRM） stage and damage imaging diagnosis（DID） stage allows for real-time monitoring and long term tracking of the structural integrity of composite aircraft structures. DRM stage using spearman rank correlation coeffi cient was introduced to generate a damage index which can be used to monitor the trend of damage extension. The DID stage based on canonical correlation analysis aimed at intuitively highlighting structural damage regions in two-dimensional images. The DRM and DID stages were trialed by an in-service SHM experiment of CFRP T-joint. Finally, the detection capability of the in-service SHM technology was verified in the SHM experiment of a full-scale composite horizontal tail. Experimental results show that the rapid monitoring method effectively monitors the damage occurrence and extension tendency in real time; damage imaging diagnosis results are consistent with those from the failure model of the composite horizontal tail structure.展开更多
Developing a versatile and durable photothermal coating is of great interest for the future large-scale utilization of abundant solar energy.However,existing photothermal coatings that rely on the assembly of rigid so...Developing a versatile and durable photothermal coating is of great interest for the future large-scale utilization of abundant solar energy.However,existing photothermal coatings that rely on the assembly of rigid solar absorbing nano-materials have often limitations in both mechanical robustness and multienergy coupling for sustainable heating.Here,we report a ready-to-use and ultratough adhesive heating patch with the integrated abilities of photo-/electrothermal conversion and damage self-diagnosis.The patch is composed of interconnected carbon microfibers filtrated with photo-curable self-adhesive resin that can readily stick on the substrate,and postcuring rapidly toughens the patch resulting in high mechanical toughness(9.1 MJ/m^(3)),high electrical conductivity(5 S/cm),strongadhesion(0.7-4.6MPa),as well as high solar-thermal conversion(∼85.3%)and Joule heating(81.6◦C/W)efficiencies.To demonstrate its application,the adhered patch on oil pipelines can rapidly reduce the viscosity of heavy crude oil to∼100Pa s under the action of solar radiation or Joule heating,allowing for oil’s easy flow in pipelines in all-weather and,thus,alleviating the use of expensive fired heaters at heating stations.展开更多
基金the Nature Science Foundation of Hebei Province Grant No.E2020402060Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province(Hebei University of Engineering)under Grant 202206.
文摘Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operationposes a significant threat to the safety of both life and property. Consequently, it becomes imperative to conductdamage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types ofpotential damage, and the presence of similar vibration data in adjacent locations make it challenging to achievesatisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmentalnoise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and antinoisecapabilities of bleacher damage diagnosis, this paper proposes improvements to the existing ConvolutionalNeural Network with Training Interference (TICNN). The result is an advanced Convolutional Neural Networkmodel with superior accuracy and robust anti-noise capabilities, referred to as Enhanced TICNN (ETICNN).ETICNN autonomously extracts optimal damage-sensitive features from the original vibration data. To validatethe superiority of the proposed ETICNN, experiments are conducted using the bleacher model from Qatar Universityas the subject. Comparative studies under identical experimental conditions involve TICNN, Deep ConvolutionalNeural Networks with wide first-layer kernels (WDCNN), and One-Dimensional ConvolutionalNeural Network (1DCNN). The experimental findings demonstrate that the ETICNN model achieves the highestaccuracy, approximately 99%, and exhibits robust classification abilities in both Phases I and II of the damagediagnosis experiments. Simultaneously, the ETICNN model demonstrates strong anti-noise capabilities, outperformingTICNN by 3% to 4% and surpassing other models in performance.
基金Gansu Science and Technology Key Project under Grant No.2GS057-A52-008
文摘Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.
基金Supported by the National Natural Science Fundation of China (51079136)(51179179)
文摘In the exploitation of ocean oil and gas, many offshore structures may be damaged due to the severe environment, so an effective method of diagnosing structural damage is urgently needed to locate the damage and evaluate its severity. Genetic algorithms have become some of the most important global optimization tools and been widely used in many fields in recent years because of their simple operation and strong robustness. Based on the natural frequencies and mode shapes of the structure, the damage diagnosis of a jacket offshore platform is attributed to an optimization problem and studied by using a genetic algorithm. According to the principle that the structural stiffness of a certain direction can be greatly affected only when the brace bar in the corresponding direction is damaged, an improved objective function was proposed in this paper targeting measurement noise and the characteristics of modal identification for offshore platforms. This function can be used as fitness function of a genetic algorithm, and both numerical simulation and physical model test results show that the improved method may locate the structural damage and evaluate the severity of a jacket offshore platform satisfactorily while improving the robustness of evolutionary searching and the reliability of damage diagnosis.
文摘The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.
文摘An improved CHC algorithm is proposed in the paper and it could be used for the damage diagnosis of structures. It breaks the bottle neck of genetic algorithm in the damage diagnosis of large structures and takes a shorter time than the SGA (Stan- dard Genetic Algorithm) in diagnosing structural damage with the same level of error. The case studies show that the algorithm is rapid in convergence and produces satisfactory results in diagnosing both fixed-end beams and jacket offshore platforms.
基金the Shenzhen Science and Technology Research and Development Funds(No.JCYJ20190806155409104)National Natural Science Foundation of China(Nos.52150222,21672130 and 52073163)+1 种基金Guangdong Basic and Applied Basic Research Foundation(No.2019A1515110356)the Qilu Young Scholars Program of Shandong University.
文摘Mitochondrial damage is closely related to the occurrence of many diseases.However,accurate monitoring and reporting of mitochondrial damage are not easy.Here,we developed a small molecule fluorescent probe named CB-Cl,which has splendid spectral properties(large Stokes shift,strong affinity for RNA,etc.)and excellent targeting ability to intracellular mitochondria.After mitochondria were damaged by external stimuli,CB-Cl would light up the nucleolus as a signal reporter.The cascade imaging of mitochondria and nucleolus using CB-Cl can monitor and visualize the mitochondrial status in living cells in real-time.Based on the above advantages,the probe CB-Cl has reference significance for the related research of mitochondrial damage and the prevention and treatment of related diseases.
基金sponsored by the National Natural Science Foundation of China(Nos.51921003 and 51905266)the Natural Science Foundation of Jiangsu Province,China(No.BK20190418)+4 种基金the China Postdoctoral Science Foundation(No.2019M661819)the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures,China(Nanjing University of Aeronautics and Astronautics,No.MCMS-I-0521K01)the Priority Academic Program Development of Jiangsu Higher Education Institutions of Chinathe Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(No.KYCX22_0347)the Interdisciplinary Innovation Fund for Doctoral Students of Nanjing University of Aeronautics and Astronautics,China(No.KXKCXJJ202208)。
文摘Structural health monitoring-based quantitative damage diagnosis technique plays a key role in real-time condition monitoring.Among the current research,piezoelectric(PZT)sensor and Guided Wave(GW)based damage quantification methods are promising,which normally establish a calibration model between GW feature and damage degree by experiments on batch specimens,and then conduct the calibration model on the monitored specimen.However,the accuracy of PZT and GW based damage quantification is affected by dispersion introduced by sensor network performance,structural material,and damage propagation among the adopted batch specimens.For improving the accuracy of damage quantification,this paper adopts PZT layer as sensor network and creatively implements theoretical and experimental research on batch PZT layers consistency control.On one hand,a two-level consistency control method based on multidimensional features-Euclidean distance is proposed to ensure the performance consistency of PZT layers placed on different specimens.On the other hand,experimental research on typical aircraft lug structures is also carried out to evaluate the requirement on performance consistency of PZT layers when performing quantitative damage diagnosis,and further verify the proposed two-level consistency control method.Experimental results show that the accuracy of damage quantification raises by 38% when the dispersion of different PZT layers is controlled within 5%.
基金Funded by the National Natural Science Foundation of China(Nos.11172053 and 91016024)the New Century Excellent Talents in University(NCET-11-0055)the Fundamental Research Funds for the Central Universities(DUT13ZD(G)06)
文摘In-service structural health monitoring（SHM） technologies are critical for the utilization of composite aircraft structures. We developed a Lamb wave-based in-service SHM technology using built-in piezoelectric actuator/sensor networks to monitor delamination extension in a full-scale composite horizontal tail. The in-service SHM technology combine of damage rapid monitoring（DRM） stage and damage imaging diagnosis（DID） stage allows for real-time monitoring and long term tracking of the structural integrity of composite aircraft structures. DRM stage using spearman rank correlation coeffi cient was introduced to generate a damage index which can be used to monitor the trend of damage extension. The DID stage based on canonical correlation analysis aimed at intuitively highlighting structural damage regions in two-dimensional images. The DRM and DID stages were trialed by an in-service SHM experiment of CFRP T-joint. Finally, the detection capability of the in-service SHM technology was verified in the SHM experiment of a full-scale composite horizontal tail. Experimental results show that the rapid monitoring method effectively monitors the damage occurrence and extension tendency in real time; damage imaging diagnosis results are consistent with those from the failure model of the composite horizontal tail structure.
基金This work is supported by National Science Foundation of China(NSFC)(Nos.21991123,51873035,and 51733003)“Qimingxing Plan”(19QA1400200).
文摘Developing a versatile and durable photothermal coating is of great interest for the future large-scale utilization of abundant solar energy.However,existing photothermal coatings that rely on the assembly of rigid solar absorbing nano-materials have often limitations in both mechanical robustness and multienergy coupling for sustainable heating.Here,we report a ready-to-use and ultratough adhesive heating patch with the integrated abilities of photo-/electrothermal conversion and damage self-diagnosis.The patch is composed of interconnected carbon microfibers filtrated with photo-curable self-adhesive resin that can readily stick on the substrate,and postcuring rapidly toughens the patch resulting in high mechanical toughness(9.1 MJ/m^(3)),high electrical conductivity(5 S/cm),strongadhesion(0.7-4.6MPa),as well as high solar-thermal conversion(∼85.3%)and Joule heating(81.6◦C/W)efficiencies.To demonstrate its application,the adhered patch on oil pipelines can rapidly reduce the viscosity of heavy crude oil to∼100Pa s under the action of solar radiation or Joule heating,allowing for oil’s easy flow in pipelines in all-weather and,thus,alleviating the use of expensive fired heaters at heating stations.
