In this work, a 532 nm diode CW laser is used to heat samples used as building materials at a 1 meter standoff distance while using an FLIR (Forward-Looking Infrared) thermal camera to monitor and record the heating a...In this work, a 532 nm diode CW laser is used to heat samples used as building materials at a 1 meter standoff distance while using an FLIR (Forward-Looking Infrared) thermal camera to monitor and record the heating and then cooling of each sample after lasers are switched off. The data is then analyzed using FLIR proprietary software. Since the absorption spectra of materials are unique, using multiple lasers of different wavelengths to simultaneously shine onto the sample at different locations would give enough thermal data to successfully characterize the samples within a reasonable amount of time. The results are very promising for applications involving non-destructive detection and classification of materials.展开更多
Manual inspections of infrastructures such as highway bridge, pavement, dam, and multistoried garage ceiling are time consuming, sometimes can be life threatening, and costly. An automated computerized system can redu...Manual inspections of infrastructures such as highway bridge, pavement, dam, and multistoried garage ceiling are time consuming, sometimes can be life threatening, and costly. An automated computerized system can reduce time, faulty inspection, and cost of inspection. In this study, we developed a computer model using deep learning Convolution Neural Network (CNN), which can be used to automatically detect the crack and non-crack type structure. The goal of this research is to allow application of state-of-the-art deep neural network and Unmanned Aerial Vehicle (UAV) technologies for highway bridge girder inspection. As a pilot study of implementing deep learning in Bridge Girder, we study the recognition, length, and location of crack in the structure of the UTC campus old garage concrete ceiling slab. A total of 2086 images of crack and non-crack were taken from UTC Old Library parking garage ceiling using handheld mobile phone and drone. After training the model shows 98% accuracy with crack and non-crack types of structures.展开更多
The non-destructive testing(NDT)of debonding in stainless steel composites plate(SSCP)is performed by infrared thermography,finite element analysis(FEA)software ANSYS is taken as the simulative tool,and 2D simulative ...The non-destructive testing(NDT)of debonding in stainless steel composites plate(SSCP)is performed by infrared thermography,finite element analysis(FEA)software ANSYS is taken as the simulative tool,and 2D simulative model has been set up to investigate effect of the thickness of coating and/or substrate on the detectibility of debonging in SSCPs.Two parameters,namely the maximum defect temperature difference and defect appearing index,are defined to evaluate the detectivity of defects,and their computational methods and formulas are given respectively.The preliminary changing tendency of the maximum defect temperature difference and defect appearing index with the thickness of coating and/or substrate is found by numerical simulation.展开更多
Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is propose...Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.展开更多
Infrared windshear prediction is one of airborne LLWS remote sensing methods.Before it is applied,the prediction system should be tested on ground to prove it’s feasibility.The LLWS simulation system isused to check ...Infrared windshear prediction is one of airborne LLWS remote sensing methods.Before it is applied,the prediction system should be tested on ground to prove it’s feasibility.The LLWS simulation system isused to check whether the infrared windshear prediction system operate properly.In this paper,according tothe requirement of the LLWS detection and the characteristic of LLWS＂source＂,we will analyze the theoryof the LLWS simulation and give a basic description of the system construction in which we pay more atten-tion to optical simulation and flight simulation.The optical simulation is used to simulate the atmospherc in-frared radiation as a LLWS occurs.The flight simulation is used to simulate the aircraft airspeed,pitch,alti-tude etc..The works presented here are necessary for the LLWS infrared prediction system.展开更多
文摘In this work, a 532 nm diode CW laser is used to heat samples used as building materials at a 1 meter standoff distance while using an FLIR (Forward-Looking Infrared) thermal camera to monitor and record the heating and then cooling of each sample after lasers are switched off. The data is then analyzed using FLIR proprietary software. Since the absorption spectra of materials are unique, using multiple lasers of different wavelengths to simultaneously shine onto the sample at different locations would give enough thermal data to successfully characterize the samples within a reasonable amount of time. The results are very promising for applications involving non-destructive detection and classification of materials.
文摘Manual inspections of infrastructures such as highway bridge, pavement, dam, and multistoried garage ceiling are time consuming, sometimes can be life threatening, and costly. An automated computerized system can reduce time, faulty inspection, and cost of inspection. In this study, we developed a computer model using deep learning Convolution Neural Network (CNN), which can be used to automatically detect the crack and non-crack type structure. The goal of this research is to allow application of state-of-the-art deep neural network and Unmanned Aerial Vehicle (UAV) technologies for highway bridge girder inspection. As a pilot study of implementing deep learning in Bridge Girder, we study the recognition, length, and location of crack in the structure of the UTC campus old garage concrete ceiling slab. A total of 2086 images of crack and non-crack were taken from UTC Old Library parking garage ceiling using handheld mobile phone and drone. After training the model shows 98% accuracy with crack and non-crack types of structures.
基金the National Natural Science Foundation of China(No.51075388)the Fundamental Research Funds for the Central Universities (No.2009KJ05)
文摘The non-destructive testing(NDT)of debonding in stainless steel composites plate(SSCP)is performed by infrared thermography,finite element analysis(FEA)software ANSYS is taken as the simulative tool,and 2D simulative model has been set up to investigate effect of the thickness of coating and/or substrate on the detectibility of debonging in SSCPs.Two parameters,namely the maximum defect temperature difference and defect appearing index,are defined to evaluate the detectivity of defects,and their computational methods and formulas are given respectively.The preliminary changing tendency of the maximum defect temperature difference and defect appearing index with the thickness of coating and/or substrate is found by numerical simulation.
基金the National Natural Science Foundation of China (Grant Nos.51575516 and 51605481)Xi'an Science and Technology Project(Grant No. 2017089CG/RC052 HJKC001).
文摘Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.
文摘Infrared windshear prediction is one of airborne LLWS remote sensing methods.Before it is applied,the prediction system should be tested on ground to prove it’s feasibility.The LLWS simulation system isused to check whether the infrared windshear prediction system operate properly.In this paper,according tothe requirement of the LLWS detection and the characteristic of LLWS＂source＂,we will analyze the theoryof the LLWS simulation and give a basic description of the system construction in which we pay more atten-tion to optical simulation and flight simulation.The optical simulation is used to simulate the atmospherc in-frared radiation as a LLWS occurs.The flight simulation is used to simulate the aircraft airspeed,pitch,alti-tude etc..The works presented here are necessary for the LLWS infrared prediction system.
