Conventional ultrasonic testing uses echo amplitude to characterize defect characteristics. Ultrasonic time-of-flight diffraction (TOFD) method detects defects by receiving the diffraction wave signal and uses the arrival time of the echo to characterize the defects. It is a highly accurate non-destructive testing method. Laser ultrasonic is a new type of noncontact ultrasonic excitation technology, which can obtain a wide frequency band signal without coupling and can simultaneously excite ultrasonic waves of various modes such as surface wave, transverse wave and longitudinal wave on the surface of the material. Among them, the surface wave and the longitudinal wave are not suitable for the TOFD detection because of their characteristics, so the transverse wave is used for defect detection in this paper. In this study, the finite element software ABAQUS was used to simulate the process of laser ultrasonic defect detection. According to the TOFD signal obtained by simulation, the size of the defect was calculated and compared with the actual size, and the detection error was obtained. At the same time, the effects of different defect length, width and depth on the echo signals are analyzed. The results show that the laser ultrasonic-TOFD method has good detection ability for defects with moderate length and small width, which indicates that it is feasible to apply TOFD method to laser ultrasonic flaw detection.
The use of spatial coding schemes is always a research hotspot of structural light 3D reconstruction. Spatial coding only needs one frame of image to reshape the three-dimensional feature of the object. However, it is difficult to obtain higher resolution due to fewer feature points extracted. In the coding stage, this paper uses a two-dimensional discrete pseudorandom pattern composed of rectangular color elements. And in the decoding stage, a feature detector for a rectangular grid point and a center point is proposed by using four corner points and a center point of a rectangle as feature points. It can get more feature points in the spatial coding without increasing the calculation amount during the decoding stage, thereby obtaining more accurate feature information on the surface of the object. From the experimental results, this method compared with the existing approaches can significantly improve the accuracy of rectangular grid points detection and can reconstruct more high-precision feature points.
Image segmentation is the most fundamental part of computer vision, which is the foundation of all other methods of image processing. The quality of image segmentation technology will affect the subsequent processing considerably. Comparing with traditional image segmentation algorithms, image segmentation algorithm based on deep learning is constantly proposed, with high performance and efficiency. But there is also a lot of room for improvement. For example, key parts such as fastening bolt are usually small in size, polluted and covered, and do not have enough characteristic information, so it is difficult to obtain satisfactory results. These factors affect the accuracy of the test, which is easy to cause serious accidents. As traditional methods sometimes cannot meet the requirement of high-accuracy result, deep learning play a particularly important role in facing those problems. To solve the problem that traditional object recognition methods are not robust enough to extract image features, parts recognition accuracy is low, and segmentation is not possible, we have made some modifications based on Mask R-CNN. In this method, convolutional neural network is used to extract features from part images. Then we use some annotated images from dataset to fine-tuned Mask R-CNN network to guarantee the accuracy. At the same time, data enhancement and k-folding cross-validation are carried out to improve the robustness of the model. Finally, the result of part recognition and segmentation by building the experimental platform proves the significance of the method.
As one of the most important transportation, the safety of railway is paid much attention to. The quality of wheel should
be checked periodically, especially in high-speed application. Normally, Non Destructive Testing (NDT), such as
ultrasonic inspection method, is applied on wheels to find the defect. A stationary automatic railway wheelset inspection
system by using ultrasonic technique is described in this paper. The phased array ultrasonic technique and wheel defect
inspection method is described in detail. Specially designed line is installed for wheelset transportation. Wheelset lifting
and rotating device is used for wheelset loading, unloading and rotating. A steel frame with complicated mechanical
structure and ultrasonic devices are designed for wheelset defect detecting. System ultrasonic performance, system
working flow, system control networking, data processing and results displaying are also described in the paper. Now,
the system is installed in Chinese EMU maintenance center for disassembled wheelset inspection and the safety of
wheels is well protected.
Pantograph sliding plate is the most important electricity-collecting part in locomotive power supply system. Once the
sliding plates are disabled, they will be severe dangerous for safety. The measurement for pantograph of 27.5KV is
especially difficult. The article uses non-contact and online dynamic detection by utilizing CCD technique to solve the
problem. The system will get all images of sliding plates after triggering by space arrangement of CCD cameras
cooperated with flashlights. The precision of demarcate is guaranteed by special methods. It adopts directional edge
search to get sliding plates, and connect the images of different CCDS. It also makes use of conditional Hough
transformation to locate the wire. The wear on sliding plates will be given after complicated processing. The system is
applicable to the detection for all kinds of pantographs by adding different arithmetic amends. At last the precision can
achieve ±0.5mm . At the same time a database is setup which can give the trend curve of wear, it can predict the limit
time of the sliding plates.
In this paper, the importance of the contact line height and gradient in the electrified railway and the current inspection methods for the contact line height and gradient are analyzed, and then the dynamic detection system for that is deigned, which based on the laser phase ranging principle. The detection system is setting on the top of locomotive and a cooperative target is fixed on the pantograph; the laser system measures the height between the top of locomotive and the working pantograph by the cooperative target when the locomotive runs, and then the gradient of contact line can be calculated in real time when the locomotive's running information is provided. The laser phase ranging system uses the DFT method to calculate the phase difference, which can get the higher resolution than the method of the electronic phase demodulation and reduce the influence of the shift of laser intensity etc. The dynamic detection system works well to detect the contact line gradient, without influencing the normal operation of the locomotive, and the disadvantages of manual detecting and detecting car are avoided.
The railway line profile measurement is necessary for the safety of the train. This article expounds a method of railway line profile measuring using laser ranging and laser scanning technology with high precision and speed. With this method, the obstacle near the track can be found out and the hidden trouble can be removed. In tunnel, the crack and deformation on the tunnel wall can be measured. The parameter of the track and contact wire can be also inspected, such as rail gauge and superelevation, position of contact wire (stagger and height), wire wearing.