The purpose of this paper is to present a multi-sensor approach to the detection and inspection of steel bars in reinforced concrete structures. In connection with our past experience related to non-destructive testing of different materials, we propose using two potentially effective methods: active infrared thermography with microwave excitation and the eddy current technique. In this article active infrared thermography with microwave excitation is analyzed both by numerical modeling and experiments. This method, based on thermal imaging, due to its characteriatics should be considered as a preliminary method for the assessment of relatively shallowly located steel bar reinforcements. The eddy current technique, on the other hand, allows for more detailed evaluation and detection of deeply located rebars. In this paper a series of measurement results, together with the initial identification of certain features of steel reinforcement bars will be presented.

In this paper, a new inspection method, joint scanning laser thermography (JSLT) as well as its data reconstruction and processing algorithm, is proposed. The new inspection method is utilized to detect and characterize the flatbottom holes (FBH) in carbon fiber composites by using joint laser scanning scheme. By analyzing the nature of the thermal image sequences sampled under such a scanning scheme, a quick and simple reconstruction method is developed to characterize the buried depth of defects based on 1D heat conduction model. The processed thermal images are expected to get higher temporal resolution and spatial resolution. It can inspect larger area within shorter acquisition time than the pulse thermography. Thus, the study solves the dilemma between the inspection speed and the inspection capacity. Later, a joint laser scanning thermography test is set up to test the algorithm on a carbon fiber composite panel with defects buried at different depth. The experimental results show that the reconstructed data almost behave as those under pulse excitation. The tendency of temperature to change in the logarithmic domain over time is similar to the curve in the TSR method. But, unlike the pulse thermography data, the defect detection rate of PCA based on reconstructed data is higher than that of fast Fourier transform (FFT) amplitude image, independent component analysis (ICA) and FFT phase image. The JSLT system is used to detect FBH and the diameter-depth ratio reached 3.33.

Impact damage, caused by low-energy impact, is inevitable during the whole life time of carbon fiber reinforced plastic (CFRP) material. However, the barely visible impact damage (BVID) is difficult to be detected by visual methods. Ultrasonic thermography (UT) is an emerging non-destructive testing technique that visualizes damage in thermal images captured by an infrared (IR) camera when the material is stimulated by ultrasound. However, noise and blurry edges around the high temperature areas may cause confusion and lead to unreliable results in the thermal images of UT test. In this work, an impact damage inspection method is proposed based on manifold learning for CFRP material. Low-power ultrasonic excitation is used for this UT. The IR image sequences are processed as data sets in high-dimensional space. These data sets are reduced to lower dimensions by manifold learning to find the intrinsic structure in the two-dimensional manifold. Each dimension of the embedding manifold correlates highly with one degree of freedom underlying the original pixel: steady and random components. The steady component, which reflects the temperature rise caused by damage, is used for VID and BVID detection. The experimental system was set up, and CFRP plate specimens with different impact damage were tested. All the impact damage could be detected and shown in reconstructed static image with little noise. The proposed method using image sequences could provide a visualized, reliable and effective impact damage inspection and localization means for CFRP material during manufacturing and in service.

In this work, multiple floral eddy current probes (FECP) are proposed. FECP consists of a detection coil and a plurality of symmetric excitation coils, and due to the number of excitation coils, it could be classified into 4-coils, 6-coils, 8-coils, or 10-coils FECP. Due to the innovation of its configuration that transmitter (TX) coils are successively connected in reverse order and distributed symmetrically around receiver (RX) coil, the proposed new probe is not only less sensitive to the change of the lift-off distance but also has high accuracy and sensitivity of detection and could resist vibration interference. In this paper, the theoretical model of FECP is established, and simulation and experiment verification are carried out respectively. Firstly, the lift-off effect is studied. Moreover, an aluminum tested plate with artificial flaws is used to evaluate the performance of differenttype of flexible FECP, which shows higher sensitivity in flaws than the conventional single coil.

Modern industry makes more and more extensive use of various composite materials. In this paper, for the purposes of various composite materials evaluation, the terahertz imaging method is presented. Basalt fibre-reinforced composites and polymeric anticorrosion coatings are considered. Basalt fibre composites are the innovative materials that are being increasingly used in modern industry. The paper also briefly introduces a specific type of complex coating of steel applied in the industry (e.g. oil or chemical). Two methods of defects detection in the mentioned structures are presented. The first method is based on a system identification, whereas the second one is on the estimation of time-domain signal parameters. Finally, the results achieved during terahertz inspection of coatings are compared with those obtained using active thermography.