Detection Of Internal Defects Research Articles

Threshold-Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic-Based Internal Defect Detection of Arc Magnets

The acoustic-based internal defect detection is essential to ensure the quality of arc magnets efficiently. Swarm decomposition (SWD) is conducive to processing acoustic signals, but it is still confronted with threshold optimization problems. Especially, the existing optimization methods for the SWD thresholds are merely available for a single signal with exclusive characteristics, instead of the various signals with similar characteristics. Therefore, a threshold-optimized SWD using grey wolf optimizer (GWO) is proposed to solve these issues and applied to detect the internal defects of arc magnets. In this method, a fitness function is designed to indicate the relationship between the SWD thresholds and the overall decomposition effect of similar signals. The minimum value of it corresponds to the threshold setting yielding the optimal decomposition. GWO is used for searching such a minimum value, and the obtained optimal threshold setting allows SWD to decompose any signal into a series of oscillatory components. The frequency information in the two oscillatory components with the highest energy ratio is extracted as the internal defect features. Random forest is carried out to identify these features. Experimentally, the detection accuracy reaches above 97%, and the detection speed per single arc magnet does not exceed 3.4 seconds. The proposed method cannot only determine the unified threshold setting of SWD for similar signals but also achieve an accurate, rapid detection for the internal defects.

MODERN DIAGNOSTICS METHODS OF MAIN PIPELINES AND PROSPECTS FOR APPLICATION OF DIGITAL TECHNOLOGIES

Background Due to the wear and tear of the main pipeline transport systems, the problem of improving the complex methods of their technical diagnostics is of paramount importance. The article provides an analysis of diagnostic methods for main pipelines, considers visual, ultrasonic, magnetic, capillary methods, as well as the prospects for the development of digital technologies in this area. It is important that each of the existing diagnostic methods allows you to identify only certain parameters of defects. The main tasks when using such methods include the assessment of the corrosion and stress-strain state of pipelines, which is directly related to the determination of the service life. One of the approaches to determining the resource of exploitation is the use of modeling and digital technologies. The ability to combine and analyze the information obtained in the process of diagnostics, which is inextricably linked with the features of operation, carrying out calculations using computer programs is a valuable achievement of our time. Aims and Objectives Analysis of the complex of diagnostic works, which includes: o detection of internal and external defects of pipe surfaces; o measurement (determination) of geometric parameters of defects; o leak detection; o detection of violations of protection zones of main pipelines; o checking the state of electrochemical protection and its effectiveness; o measurement of mechanical deformation and displacement of sections of the main pipeline; o checking the condition of pipeline fittings; o determination of the condition of the shell and the depth of the laying of the main pipewire; o measurement of pipeline wall thickness and metal hardness; o determination of pipe geometry errors. Results Methods for optimization of diagnostics of main pipelines are proposed, which consist in an integrated approach to diagnostics of the inner surface of a main pipeline using the method of acoustic emission, which allows: o identify dangerous interruptions in production and operation at an early stage and prevent their development to a critical level; o determine the degree of risk of detected defects; o conduct 100% inspection of the diagnosed area, including areas inaccessible for visual control; o estimate the remaining service life of the pipeline based on information about existing failures and damages. The perspective possibilities of using digital technologies to optimize the process of diagnostics and monitoring are considered.

Detection of internal defects in CFRP strengthened steel structures using eddy current pulsed thermography

This study investigates the effectiveness of eddy current pulsed thermography (ECPT) to detect internal defects in CFRP strengthened steel structures. Various common internal defects of CFRP strengthened steel structures are involved in this study, including delaminations, low-velocity impact damages in CFRP, cracks in steel, and debonding in the CFRP/steel interface. We theoretically analyzed the influence of these defects on the electromagnetic and thermal response of the structures. In order to verify the theoretical results, numerical and experimental investigations are performed on CFRP strengthened steel plates with various kinds of defects. Numerical and experimental results demonstrate that the investigated defects can be inspected by ECPT and indicated as low-temperature regions in the ECPT thermograms. Besides temperature features of thermograms, temperature profile evolutions and temperature evolutions are investigated to further analyze the thermal response on the CFRP surface. Moreover, the efficacy of ECPT in retrieving the shape and size of defects is demonstrated based on the experimental results. In general, theoretical, numerical and experimental results demonstrate that ECPT is effective in inspecting internal defects in CFRP strengthened steel structures, which indicates that ECPT provides a promising way for such a complicated hybrid structure.

Study on Nondestructive Test Method for Inner Defects of Mass Concrete

Due to the large geometric size and the influence of dense embedded steel bars, It is difficult to use the traditional nondestructive testing (NDT) method for evaluation the inner defects of mass concrete. A comprehensive nondestructive testing method for internal defects of mass concrete was proposed, which used elastic wave Computed Tomography (CT) scanning for general survey, ultrasonic tomography and ground penetrating radar (GPR) technology for fine investigation. The method was applied to the defect detection of a bearing platform concrete belonging to an interchange bridge. Finally, the reliability of this method was verified by the results of core drilling. The results show that the proposed method can effectively detect the distribution of defects in concrete, provided a new solution and practical guidance for the internal defect detection and reinforcement of mass concrete structure.

Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition

X-ray flaw detection is a key link in the detection of internal defects in titanium alloy castings which are used for most important components in aeroengines. However, the existing manual defect detection methods from the X-ray images have common drawbacks such as unstable artificial recognition, misdetection, misjudgment, fails of quantitative analysis, huge workload, and low-quality inspection efficiency. To avoid these drawbacks, this paper proposes a new artificial intelligent (AI) method to detect and recognize the aerospace titanium casting defects from the X-ray images. It includes the target defect positioning method named as filtered selective search algorithm (FSS) and the defect classification method named as evenly distributed convolutional neural network (ED-CNN). In the target positioning step, through statistical analysis of defect characteristics, a filtered selective search algorithm is built with two filters (size and edge curvature). In this way, the FSS algorithm can position the defects with almost 100 % of accuracy, hence avoid missed detection and false detection. In the target classification step, an ED-CNN is constructed with a similar structure of the same number of layers in each feature extraction stage, and its entire architecture is evenly distributed. Compared with other three classic high-performance convolutional neural network models (AlexNet, VGG16 and VGG19), the ED-CNN model has the best performance. The ED-CNN model was tested with 324 targets from 50 original images, a classification accuracy of nearly 90 % was obtained for low density holes, porosity, linear defects, high density inclusions and casting structure. The FSS/ED-CNN method of two phases defect detection proposed in this paper can achieve accurate positioning and high accurate classification of typical defect targets, and is expected to solve the common drawbacks of "manual defect detection". The newly-proposed FSS/ED-CNN method has important research significance and engineering value.

Quantitative method for detecting internal and surface defects in wire rope

The detection of internal and external defects in steel wire rope is a very important task. Current nondestructive testing methods cannot distinguish between internal and surface defects, and cannot determine the quantitative characteristics of internal defects. This paper first analyzes the advantages and disadvantages of induction coil magnetic flux detection and Hall sensor-based magnetic flux leakage detection. The problem of simultaneous detection using these two methods is then solved, and a new quantitative detection method is proposed for internal and surface defects in wire rope. Finally, a calculation method that uses a two-step training algorithm to establish two sets of neural networks is proposed. Results from simulations and experiments verify that the proposed method can accurately distinguish between internal and surface defects in wire rope, and can also quantitatively detect the width, cross-sectional loss rate, and depth of the defects.

Analysis of Impact Characteristics and Detection of Internal Defects for Unidirectional Carbon Composites with Respect to Fiber Orientation.

With the increasing use of carbon fiber reinforced plastics in various fields, carbon fiber composites based on prepregs have attracted attention in industries and academia research. However, prepreg manufacturing processes are costly, and the strength of structures varies depending on the orientation and defects (pores and delamination). For the non-contact evaluation of internal defects, the lock-in infrared thermography was proposed to investigate the defects in the composites subjected to the compression after impact test (CAI). The drop-weight impact test was conducted to study the impact behavior of the composites according to fibers orientation for composite fabricated using unidirectional (UD) carbon fiber prepregs. Using CAI tests, the residual compressive strengths were determined, and the damage modes were detected using a thermal camera. The results of the drop weight impact tests showed that the specimen laminated at 0° suffered the largest damage because of susceptibility of the resin to impact. The specimens with 0°/90° and +45°/−45° fibers orientation exhibited more than 90% of the impact energy absorption and good impact resistance. Furthermore, the specimens that underwent the impact tests were subjected to compressive test simultaneously with the lock-in thermography defects detection. The results showed that internal delamination, fibers splitting, and broken fibers occurred. The temperature differences in the residual compression tests were not significant.

基于微波层析成像的活立木内部缺陷检测

基于微波层析成像的活立木内部缺陷检测

Combined Convolutional and LSTM Recurrent Neural Networks for Internal Defect Detection of Arc Magnets Under Strong Noises and Variable Object Types

This paper focuses on developing the acoustic-based detection of arc magnet internal defects under different noises and object types. The major challenge in such a detection case is that the traditional approaches mainly rely on expert knowledge and experiential features, which results in a poor generalization performance of the algorithm for new types of detection objects and is susceptible to noise interference. This work presents a novel detection framework based on an end-to-end one-dimensional (1D) convolutional long short-term memory (LSTM) model, where both the spatial and temporal features of the measured acoustic signals are extracted and then jointed for determining the internal defects of arc magnets. In addition, the LSTM layers are employed behind the 1D convolutional neural network, which makes the number of time steps in the LSTM layers for the temporal feature extraction is much smaller than the length of the input segments, thus the computational complexity of the LSTM layers can be highly reduced. Experimental results show that our method is superior to existed methods in the detection accuracy for the internal defects of arc magnets, and the diagnosis time per a single arc magnet is controlled at the millisecond, making it appropriate for real-time applications. Furthermore, the robustness of the proposed framework is validated through experiments on different signal-to-ratios and multiple object types of arc magnets.

An Ultra-Wideband Microwave Diagnostic Method for Detecting Cracks in Polymeric Insulators

To realize an embedded online diagnosis system for accurate detection of internal defects in high voltage polymeric insulators, a novel ultra-wideband (UWB) microwave diagnostic system for fault detection is proposed in this article. The UWB system is based on the multimode transferring theory on dielectric waveguides, where the level difference of transmission signals of mode transferring has been employed to detect internal defects, such as cracks. First, an investigation by simulations is performed regarding the interfacial millimeter-order cracks in both transverse and longitudinal orientations. Differences of 30-50 and 20-40 dB in receiving signals of mode-transferring between non-crack and crack cases have been observed over 3-10 GHz to transverse and longitudinal oriented cracks, respectively. Then, a test prototype is designed and manufactured for experimental verification. The measurement results have a significant 40-60 dB receiving signal level difference between the non-crack reference case and the crack case. The proposed method is promising for further development of online systems to detect millimeter-order cracks in energized polymeric insulators.

Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy

A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity. In this study, wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits. Feature selection methods were adopted here to achieve a significant selection of wavelengths. To measure the goodness of the model, the dataset was collected using the NIR (Near Infrared) spectroscopy with wavelength ranging from 673 nm–1900 nm. The classification was performed using Euclidean distance measure both in the original feature space and in FLD (Fisher's Linear Discriminant) transformed space. The experimental results showed that the lower range wavelength (673 nm–1100 nm) was the efficient wavelength for the detection of internal defects in mangoes. Further to express the effectiveness of the model, different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits. The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterion with a classification accuracy of 84.5%. This study showed that NIR system is a useful technology for the automatic mango fruit assessment which has the potential to be used for internal defects in online sorting, easily distinguishable by those who do not meet minimum quality requirements.

Detection of Internal Defects of Full-Size Composite Insulators Based on Microwave Technique

In this article, a detection method based on microwave technique is proposed to detect the internal defects of composite insulators. Based on this method, an automatic detection system is developed for full-size composite insulator. In the experiments with 110-kV composite insulators, three types of defects, namely the air-gap defect, carbonization defect, and conductive defect, are artificially made, and they are located exactly by the developed automatic defect detection system. It is found that, due to the dielectric constant and magnetic permeability properties, the conductive defect can be detected easily, whereas the carbonization defect is the most difficult one to be detected among all three defects. In addition, a real defective 500-kV composite insulator from transmission line after years of operation is also detected by the developed system and the results are consistent with the infrared imaging method. According to the test results, the developed system is efficient, labor-saving, and robust. It can have a great potential in the quality control of composite insulators.

Casting X-ray image segmentation based on deep learning

Aiming at the problems that the current image segmentation algorithm has low accuracy in realizing the internal defect segmentation of industrial castings and the algorithm is not lightweight enough, an improved industrial casting internal defect detection algorithm Effi-DeepLab based on DeepLabv3+ was proposed. This method used MBConv in EfficientNet to replace the original Xception module for feature extraction, making the feature extraction network more efficient and lighter. Aiming at the problem of small internal defects in industrial castings, the expansion rate of the hollow convolution in the atrous spatial pyramid pooling (ASPP) layer was redesigned. The convolution block was more robust to small targets; the low-order semantic information in the feature extraction stage was fully utilized at the decoding end to perform multi-scale feature fusion to improve the accuracy of small target defect segmentation. The experimental results show that the segmentation accuracy and mIoU of the Effi-DeepLab model in the internal defect image data set of the automobile wheel are 93.58% and 89.39%, respectively, which are improved by 2.65% and 2.24%, respectively, compared with DeepLabv3+, and has better segmentation effect. In addition, it is experimentally verified that the proposed algorithm has good generalization.

A study of detection of internal defects in CFRP using different modes of planar laser

The detection for internal defects of the practical aviation carbon fiber reinforced polymer (CFRP) using different modes of laser was discussed in this paper. The results show that the effect of the laser with long pulsed wave heating mode is the best, and then sawtooth wave mode is followed by sine wave mode. Besides, the valid image processing method plays a significant role in improving the effect of differential laser infrared thermography. Compared with the detection results of thermal lamp thermography, the effect of differential laser thermography is better. There are two main advantages of using a planar laser to heat the aviation carbon fiber composites: (1) the detection speed is so fast, which only takes a few seconds to complete the whole detection process; (2) laser method is more flexible than thermal lamp thermography because of the range detected can be adjusted according to the aim of experiments.

Characterizing Coupled Transport Phenomena in Large-Format Lithium-Ion Pouch Cells Using Lock-in Thermography and Laser Scanning

Management of battery systems is difficult due to the variety of physical processes that occur when a battery is put under an electrical load. These processes produce macroscopic side effects, such as stress/strain and temperature evolution, which originate at the electrode level. The electrochemical-thermal-mechanical phenomena in battery cells are highly coupled in applications such as electric vehicles. Because microscopic effects in battery electrodes can propagate into larger length scales such as the cell and pack levels, there are changes on the outside that can be observed.Contact-free, non-destructive methods for measuring changes in battery temperature and strain could serve as tools to determine a battery’s second-life prospects, and could also aid early fault detection. The lock-in thermography technique has been used to determine faults and locate defects within cells.1 It has also been shown that mechanical stresses can be used to monitor state of health (SOH) and state of charge (SOC).2,3 Laser scanning has also been implemented to generate strain distributions showing local mechanical effects during electrochemical cycling.4 A first step in understanding highly coupled systems is to characterize the individual phenomena in isolation. Figure 1 presents a contour plot that shows the result of a zero-displacement experiment, where the evolution of equilibrium stress within a constrained 3-cell battery pack is measured at various SOCs and at various ambient temperatures. This experiment has shown that thermally induced volume change can contribute as much to mechanical forces within a cell as intercalation/deintercalation stresses. In high-power applications such as electric vehicles, we expect to see consequences from both of these factors. So, we will be developing an experimental set-up that combines the techniques of lock-in thermography and laser scanning to simultaneously capture 2D strain and temperature distributions that arise in response to different current input signals. Data from the individual techniques, plus the combined experiment, can be used to see how a battery’s response changes as a consequence of its thermal and mechanical state, and how the relative contributions of temperature and strain can affect battery operation.References Robinson, J. B. et al. Detection of Internal Defects in Lithium-Ion Batteries Using Lock-in Thermography. ECS Electrochem. Lett. 4, A106–A109 (2015).Cannarella, J. & Arnold, C. B. State of health and charge measurements in lithium-ion batteries using mechanical stress. J. Power Sources 269, 7–14 (2014).Mohtat, P., Lee, S., Siegel, J. B. & Stefanopoulou, A. G. Towards better estimability of electrode-specific state of health: Decoding the cell expansion. J. Power Sources 427, 101–111 (2019).Rieger, B. et al. Multi-directional laser scanning as innovative method to detect local cell damage during fast charging of lithium-ion cells. J. Energy Storage 8, 1–5 (2016). Figure 1

Non-contact detection of delamination in stainless steel/carbon steel composites with laser ultrasonic

This paper proposed the non-contact non-destructive detection of internal delamination defects in stainless steel/carbon steel laminated strip with laser ultrasonic detection method. The finite element model of stainless steel/carbon steel laminated plates was built to analyze the propagation of laser ultrasound in stainless steel/carbon steel laminated plates. The influences of delamination defects on laser ultrasonic wave field distribution and delamination size on the amplitude of laser ultrasound waves were analyzed to investigate the interaction between laser ultrasound and delamination defects. The out-of-plane displacements which were very sensitive to the delamination defects of laser ultrasonic waves were used to characterize delamination defects. The experimental signals were in good agreement with simulation waveforms. The laser ultrasonic method based on waveform amplitude is effective for non-contact detection of delamination in stainless steel/carbon steel laminated strip, which can provide data support for the optimization of rolling process.

SUBSTANTIATION OF THE CHOICE OF METHODS OF NON-DESTRUCTIVE TESTING OF ELEMENTS OF ENERGY EQUIPMENT USING A FUZZY LOGIC APPARATUS

The paper illustrates the solution of the problem of choosing methods of quality control of manufacturing parts and assemblies of power equipment using a fuzzy logic device. The main methods of non-destructive testing for the detection of surface and internal defects are considered, as well as the main indicators of quality of metal products. The types of metal defects and welded joints are inspected. The description of the equipment and means of control for detection of defects is executed. The sequence and methods of quality control by ultrasonic, capillary and magnetic powder methods of control are described in detail. The results of quality control of parts during production and during their operation are obtained. The analysis of the revealed defects is carried out. An example of using an integrated approach to control is given. The obtained results of control of the percentage of coincidence of detection of defects on the product are analyzed. Comprehensive quality control was performed by visual, ultrasonic, capillary and magnetic powder methods of non-destructive testing to determine the percentage of coincidences of defects. By creating a heuristic analyzer based on the interface of the fuzzy logic system Fuzzy Logic Toolbox of the Matlab program, an example of determining a combination of non-destructive testing methods for quality control of a steam turbine bearing liner is considered. Computer simulation according to the Mamdani algorithm is carried out, which consists of fazzification with determination of ranges of change of input values for each example, task of distribution functions for each input parameter; calculation of rules based on the adequacy of the model; defuzzification with the transition from linguistic terms to quantitative assessment and graphical construction of the response surface. The simulation made it possible to determine the optimal combination of non-destructive testing methods, which provides the highest quality of defect detection in the steam turbine bearing liner.

Quantitative Detection of Minor Defects in Metal Materials Based on Variation Coefficient of CT Image

In order to perform precise quantitative detection of internal minor defects in additive manufacturing of metal materials based on industrial CT, a quantitative detection method for minor defects in metal materials based on gray-scale variation coefficient of minor defects in CT image is presented. Firstly, based on the analysis of gray-scale features of minor defect regions in CT image, a minor defect image response model is constructed by combining the point spread function (PSF) and noise approximate Gaussian distribution, and the correspondence between the minor defects of different sizes and the local variation coefficient of CT image is derived, and a quantitative detection method based on the coefficient of variation of minor defect region in CT image is established for minor defects of AM metal materials. Industrial CT experiments were then conducted by processing a series of minor defect reference test blocks of different sizes with TC4 titanium. Experiment results show that the defect size and the coefficient of variation reflect a good linear relationship of monotonous increase, with correlation more than 0.996, and the quantitative results of minor defects based on the coefficient of variation method accord well with the measurement results of the microscope, with relative error controlled within 4%. Obviously, this method characterized by simple measurement process and high practical value has improved the quantitative precision of minor defects in industrial CT.

Finite element simulation of laser-generated ultrasonic waves for quantitative detection of internal defects in welds

The non-destructive detection of the weld internal defects is still worth to study. In this paper, a new method to detect weld internal defects by using the laser ultrasonic technique. The numerical simulation analysis is used to investigate whether the weld has quality defects. This paper detailed analysis the interaction between the LR-wave generated by laser and the internal defects of the weld to both sides of the weld through the B-scan image. It is proposed to use the difference B-scan image with or without internal defects to separate the LR-wave. This method can effectively separate the Lr-wave, and can provide a basis for the judgment of the internal defects of the weld. The proposed method may provide a useful way to estimate the internal defects of the weld.

Thermal imaging as a tool in food analysis

This Letter reviews the application of thermal imaging for food quality control. Thermal imaging uses a single, portable device, which provides a visible image of the invisible infrared radiation, and can be used for real-time monitoring at low cost compared to spectral imaging technology. To monitor the quality parameters of the sample, classification or regression models are constructed using multivariate analysis or chemometrics tools. Few studies have used thermography for the analysis of food to-date, so this Letter will only address thermal mapping, internal defect detection, adulteration detection and the prediction of compound concentration.