Fault Probability Research Articles

A modal decomposition imaging algorithm for ultrasonic detection of delamination defects in carbon fiber composite plates using air-coupled Lamb waves

In this paper, a modal decomposition imaging (MDI) algorithm for detecting delamination defects in carbon fiber composite plates using air-coupled Lamb waves was proposed. Compared with the traditional time-domain imaging method using amplitude difference, the proposed MDI algorithm contained modal decomposition process and rotating scanning defect probability imaging method, which was more suitable for analyzing the nonlinear and non-stationary leakage Lamb waves signal. The cross-correlation coefficient of the instantaneous energy was constructed to be the damage index, which was obtained by the pretreatment of the relatively pure A0 mode Lamb waves. The effectiveness of MDI algorithm proposed in this paper for realizing the delamination defects of carbon fiber composite plates was verified by qualitative imaging and quantitative characterization comparing with the defect probability imaging (DPI) algorithm. It would be suitable for achieving accurate characterization of defects in situ testing and large-area rapid scanning of aerospace composite plates after quickly scanning.

Multiport Hybrid DC Circuit Breaker With Reduced Fault Isolation Time and Soft Reclosing Capability

The hybrid dc circuit breaker is one of the most promising dc circuit breakers (DCCBs). However, its cost is relatively high, and the completion of its fault isolation is significantly delayed because of the large current limiting inductors configured in the multiterminal dc grid. Besides, due to the utilization of overhead lines, the probability of temporary faults is much increased, which means there is a great possibility that the power supply can be rapidly restored by reclosing DCCBs. However, the temporary faults must be distinguished from the permanent faults before the reclosing operation to avoid damaging the fragile power electronic equipment. Therefore, considering the above issues, a novel multiport hybrid dc circuit breaker (MHCB) is proposed in this article. The MHCB mainly has three advantages: the reduced implementation cost by sharing the expensive main breaker among multiple lines, the reduced fault isolation time by bypassing the current limiting inductor during energy dissipation, and the ability to reclose softly. Extensive simulations and experiments verify the effectiveness of the device. In addition, the comparison of the new device and existed solutions is presented.

Transformer Fault Diagnosis Method Based on Neural Network and D-S Evidence Theory

Abstract The BP neural network is used to calculate the basic fault probability distribution of the dissolved gas in the transformer oil and the core grounding online monitoring data, and the Dempster-Shafer evidence theory is used to fuse the multi-source information of the basic probability of various types of faults to obtain a transformer fault diagnosis model. Transformer samples are used to verify the model, and the support vector machine and convolutional neural network fault diagnosis models are compared, and it is concluded that the proposed method is better in terms of fault diagnosis accuracy and stability.

Open-Circuit Fault Characteristics and Location Methods of Switch Elements For Cascaded Power Electronic Transformers

Power electronic transformers (PETs) play an important role in AC/DC hybrid distribution networks. However, the fault probability of a PET is higher because of the use of many power electronic devices. The short-circuit protection of a PET is relatively mature, but studies on its open-circuit faults are limited to insulated gate bipolar transistors (IGBTs) in the cascaded H-bridge (CHB). Therefore, other types of open-circuit faults result in dead zones in the fault location process. In this article, the open-circuit fault characteristics of switch elements at different positions of a PET are analyzed, and a protection circuit is designed for the possible overvoltage caused by the open-circuit fault in the CHB. Moreover, the fault location methods of switch element open-circuit faults are proposed. The proposed methods could adapt to the diversity of control modes, power directions and load levels. The correctness and effectiveness of the methods are verified via simulation tests. This work provides a basis for the implementation of fault-tolerant control and protection of PETs.

A Risk Assessment Method of Power Transformer Based on Three-Parameter Interval Grey Number Decision-Making

In the process of power transformer risk assessment, the loss degree index is difficult to accurately quantify due to the influence of uncertain factors, leading to the deviation of risk judgment. A power transformer risk assessment method based on the three-parameter interval grey number decision-making is proposed. Firstly, the fault probability of the transformer is quantified based on the condition evaluation results. Secondly, considering the uncertainty of DG output and load, the Nataf transform and Cholesky decomposition were used to eliminate the correlation of random variables, and a three-point estimation method combined with a DC cut load model was introduced to calculate the probability distribution of the loss degree caused by the transformer fault. Finally, the origin moment of each order was obtained based on the calculation formula of risk value, and the risk probability distribution was obtained through the Cornish–Fisher series expanding. The decision method of the three-parameter interval grey number distance measure was used to judge the risk grade of the equipment. The results show that the proposed method fully considers the influence of uncertainty on equipment risk judgment, can realize the full use of the equipment risk value interval number to judge the risk, and avoids the decision-making defects of the traditional certain risk quantification method. Meanwhile, the influence of different factors on the risk evaluation results is in line with the actual operation condition of the transformer. The results also verify the effectiveness and accuracy of the proposed method, which provides a new judgment idea for power grid equipment risk quantitative assessment.

Structural, optical, and electrical properties via two simple routes for the synthesis of multi-phase potassium antimony oxide thin films

Multi-phase ternary oxide glass thin films of potassium antimony oxide were synthesized via dip coating (DC) and spray pyrolysis (SP) methods. The growth of thin film was conducted on a non-conductive borosilicate glass substrate. Surface morphology was investigated and showed different characteristics. X-ray diffraction (XRD) analysis revealed the peaks corresponded to the monoclinic KSb3O5 and K2Sb4O11 phases. The structural parameter analysis shows that the lower values of micro strain (ε), dislocation density (δ), and stacking fault probability (SF) with higher number (N) of particle in the multi-phase thin film for the spray pyrolysis method confirm slightly larger crystallite size, less crystal imperfection, and less structural disorder. The lower average transmission, absorption, and extinction coefficients resulted to higher refractive index, permittivity, and electrical susceptibility for the sample synthesized by a spray pyrolysis method. In addition, the average energy band gap values (Eg) of 3.57 and 3.60 eV were obtained for the dip coating and spray pyrolysis methods, respectively. However, the lower Rs (14.69 × 107 Ω/sq.) with higher σh (85.09 S/cm) and the FOM(H-HR) (0.183 Ω-1) of the multi-phase K–Sb–O thin film for the dip coating method were obtained may be due to more interconnections generated at the interface and there are less residual on the surface of thin film.

Improving seismic fault mapping through data conditioning using a pre-trained deep convolutional neural network: A case study on Groningen field

Seismic fault interpretation is a crucial and indispensable step in reservoir exploration that requires substantial time. As a result, much research has been dedicated to applying deep learning in this venture. Deep learning has shown significant progress in the identification of seismic faults. However, its applicability has been hindered given the lack of appropriate labelled fault data and poor seismic imaging. The deep convolutional neural network (DCNN) employed in this study is a cutting-edge deep learning technique for image improvement and identification.In this study, detecting seismic faults and improving seismic imaging using a pre-trained DCNN is proposed using the Groningen field that has complexity in accurately imaging sub-salt geological structures as a case study. The presence of salt in the field causes wave attenuation. The fault mapping procedure is considered a segmentation of the 3D seismic problem and trains an encoder-decoder architecture, using a Deep Residual U-net, to generate a fault probability volume. A decent fault prediction result is achieved on the Groningen seismic volume. The next step of this study involved seismic data conditioning, where DCNN is applied to denoise volumes to improve visualisation, significantly below the salt structure. The DCNN, when used to improve seismic imaging, achieved a signal-to-noise ratio (SNR) of 30.2, which is almost quadruple that of the original volume. Faults were mapped on the conditioned volume using DCNN, resulting in an improved seismic fault probability volume. This technique achieved distinctively interpreted faults illustrating the significant improvements DCNN brings to the seismic imaging process. In interpreting new seismic volumes with poor imaging and low signal-to-noise ratio, caused by a change in seismic frequency and amplitude propagating through an attenuating medium like the Groningen field, researchers and geophysicists may apply the DCNN for volume conditioning before mapping seismic faults using neural networks.

Still beyond a chance: Distribution of faults in elite show-jumping horses.

This study aimed to identify factors that can influence show-jumping performance during top level competitions in the Western European League (2017/2018, CSI5*). The performance data of 144 riders and 222 horses were obtained from video records (FEI TV/ website). Riders with horses achieved a total of 9114 jumping-efforts over 320 obstacles including oxers (n = 142), oxers with water (n = 15), triple bars (n = 6), verticals (n = 136), verticals with water (n = 14) and walls (n = 7). Obstacles in the first round (FR) or in jump off (JO) were standing either as single (n = 6290) or as a combination of two or three fences in a row (n = 2824). The overall fault rate (dropping the pole or refusal/run-out to jump) was 7.85%. The probability of a fault increased with the rank of the fence in the course of FR (F (1, 7649) = 5.29, P < 0.0001, GzLMM; PROC GLIMMIX, SAS). The highest probability of a fault (F (5, 7649) = 2.51, P < 0.03) in FR was found on the vertical obstacle with water (0.125 ± 0.021, LSMEAN ± standard error) while the lowest was on the triple bar (0.037 ± 0.015) and wall (0.048 ± 0.020). Riders who completed more starts in previous competitions achieved a lower fault rate (F (1, 7649) = 6.17, P < 0.02) in FR as well as in JO (F (1, 931) = 7.05, P < 0.01). The probability of faults in JO decreased with a higher speed (F (1, 931) = 6.66, P < 0.01) but there was no significant correlation between the speed in JO and FR in individual horses (r = 0.26, P < 0.01). More faults were found on the fences within combinations in both rounds (FR, JO). The highest probability of faults was found on the first fence (FR 0.095 ± 0.016; JO 0.043 ± 0.008) or second fence (JO 0.055 ± 0.020) of the double combination compared to the least probability of faults on single obstacles (FR 0.057 ± 0.007, F (5, 7649) = 5.29, P < 0.0001; JO 0.100 ± 0.027, F (1, 931) = 3.39, P < 0.04). Other tested factors did not affect show-jumping performance. Some faults are still more likely and not random in a show-jumping course, therefore, the appropriate training focus can improve performance and safety in competitions.

Distributed relay protection for distribution network based on hybrid power method and current method

The distributed power supply is gradually connected to the distribution network, the original single power source radiant network pattern of the distribution network no longer exists. The topology of the distribution network is characterized by variable topology and multi-power supply, and the traditional fault detection and protection actions cannot adapt to the new distribution network topology. Based on failure probability of the highest single phase ground fault in distribution network by the power differential method and differential current method to deal with failure, installed the reclosing device, and through the simulink simulation, verify the effectiveness of the proposed scheme, can effectively deal with the distribution network fault, to deal with instant fault and permanent fault, effectively reduce misoperation. In this paper, the scheme of setting the setting value independently is adopted, which can quickly restore the power supply in non-fault area and improve the continuity of power supply. It has very broad engineering application scenarios.

Probability prediction method of transmission line icing fault based on adaptive relevance vector machine

To precisely forecast the operation status of transmission line during an ice storm and achieve early warning, a method based on adaptive relevance vector machine (ARVM) is proposed for fault probability prediction of transmission line icing. According to the basic theory of RVM, this paper establishes a forecasting model, which consists of selection and preprocessing of data, initial parameter optimization, icing prediction with adaptive optimization and fault probability prediction of transmission line. The quantum particle swarm algorithm, together with K-fold Cross-validation is applied to optimize model parameters. The weight vector of the icing prediction model is corrected by repeating training to get the precise prediction result of ice thickness with its probability distribution. The case study with practical data from Zhejiang province shows that the proposed method can effectively improve the accuracy of icing prediction and further realize fault probability prediction of transmission line, which can provide early warning for the anti-icing and mitigation work of the electric power department.

Visualization Techniques for Simulation-Based Dependent Failure Analysis

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Failure analysis of engineering systems typically emphasizes identification and mitigation under an independent failure assumption with dependent failures treated as the exception rather than the rule. Some frameworks for addressing dependent failures through analysis appear in standards including NUREG 0492, ISO 26262, MIL-1629-A, and ARP4761 amongst others. The purpose of identifying these dependencies is to allow system analysts to determine and quantify the factors that influence dependent fault probabilities. Once identified, failure relationships can be incorporated into a Discrete Event Simulation (DES) of the system, providing a mathematically rigorous estimate of system utility (e.g., availability, reliability). The output of a simulation can provide an expected value of performance but additionally, can also allow the analyst to identify the downstream impact of probabilistic dependencies between system elements. Detailed simulations of complex systems have the potential to generate rich data sets, which once analyzed may aid in the formulation of corrective actions to mitigate any detrimental effects identified from simulated failure dependencies. However, human factors research has demonstrated that effective decision making requires that only the key information is provided to the user, which in turn should be supported through effective data visualization techniques. In this paper, case studies are conducted using automated failure dependency mappings generated using a model-based RAMS software and then simulated using a DES framework. Key mappings and transformations of temporal data from the simulation are then presented along with various supporting visualization techniques that together aim to elevate the situational awareness of analysts when performing complex system evaluation.&lt;/div&gt;&lt;/div&gt;

Evading stress-property tradeoff in a SMA/PZT laser cladding coating via phase transformations

Tradeoff between residual stress and properties is the primary obstacle which hinder the practical application of laser cladding technology. Herein, a Fe-based SMA/PZT (shape memory alloy/lead zirconate titanate piezoelectric ceramic) composite coating was fabricated by laser cladding. To explore the optimal PZT doping amount and its performance enhancement mechanism of the composite coating, microstructure, phase composition, stacking fault probability (SFP), residual stress, microhardness and wear resistance of the coating were investigated. The results reveal that the composite coatings are composed of PZT (Pb(ZrTi)O3) phase, γ austenite phase and ε martensite phase. Among which the coating with 1 wt% PZT doping amount possesses the highest microhardness as well as the best wear resistance. The grain size and stacking fault energy (SEF) of the coating have been reduced after doping with PZT. Ultramicroscopic structure unveils that the γ → ε phase transition occurred in the SMA/PZT composite coating during the laser cladding process, high-density dislocation accumulation zone appears around the PZT particles. PZT doping has an interesting effect on both promoting and hindering the transformation of γ → ε in the composite coating. Doping of PZT increases the amount of martensite in the local area of the composite coating, reduces the residual stress, and significantly improves the microhardness and wear resistance.

Determination of the magnetic signature due to the presence of a nonlinear material property gradient in a nickel-based superalloy by thermoelectric means

It was showed that the thermoelectric coupling in a metallic medium can be applied as a viable means of inspecting inhomogeneities and defects in materials. This nondestructive sensing is carried out by using magnetometers to monitor the thermoelectric currents around the inspected area when a temperature gradient is induced inside the sample. In the NDE thermoelectric technique, the detection probability of defects in metals depends on the thermoelectric background signature produced by the intrinsic texture and inhomogeneity of the material. The measured magnetic signature is due to a combination of both effects. This paper investigate s the magnetic signature produced by a microscopic inhomogeneity such as precipitation and/or segregation when the material properties exhibit a non linear spatial variation in the cross section of a slender bar. An analytical method has been developed for calculating the magnetic field produced by the quadratic spatial dependence of the material properties. Experimental data from an inhomogeneous nickel-based superalloy 825 sample aged at 650 °C for 312 h were correlated with the theoretical predictions and fully verified the analytical model. Finally, the relationship between the strength of the magnetic signature and the complex microstructural and chemical features of this popular high-strength superalloy was studied.

Fault enhancement using probabilistic neural networks and Laplacian of a Gaussian filter: A case study in the Great South Basin, New Zealand

Fault identification is critical in defining the structural framework for exploration and reservoir characterization studies. Interpreters routinely use edge-sensitive attributes to accelerate the manual picking process, in which the actual choice of a particular edge-sensitive attribute varies with the seismic data quality and with the reflectivity response of the faulted geologic formations. The cyan-magenta-yellow (CMY) color blending provides an effective way to combine the information content of two or three edge-sensitive attributes when more than one attribute is sensitive to faults. We have evaluated whether combining the information content of more than three attributes using probabilistic neural networks (PNNs) provides any additional uplift. We use training data consisting of manually picked faults on a coarse grid of 3D seismic lines, and then, we use an exhaustive search PNN to identify the optimal set of attributes to create a fault probability volume for a 3D survey acquired over the Great South Basin, New Zealand. We construct a suite of candidate attributes using our understanding of the attribute response to faults seen in the data and examples extracted from the published literature to use the list as the analyzed attributes. Using a subset of picked faults as training data, we evaluate which suite of attributes and hyperparameters exhibits the highest validation on the remaining training data. When used together, we find that volume aberrancy magnitude, gray-level cooccurrence matrix (GLCM) homogeneity, GLCM entropy, Sobel filter similarity, and envelope best predict the faults for this data set. The PNN supervised classification creates a seismic image volume that exhibits fault probabilities providing a simple combination of multiple seismic attributes. We also find that applying a directional Laplacian of a Gaussian and skeletonization filters to the PNN fault volumes provides a superior result to simple CMY blending techniques.

Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands

Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and derisking activities. Efforts to automate the detection and extraction of geologic features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine-learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, northeast Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D prestack depth migrated seismic data set. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geologic context to the interpreter and input to dependent workflows more efficiently.

Study on Hierarchical Dynamic Adjustment of Integrated Circuit Flow Based on Nonlinear Detection

In order to solve the problem that the test time is long and the test efficiency is affected in the process of IC test. With the increase in the complexity of integrated circuits, it is difficult now to diagnose the faults. To overcome this situation, there is a need to upgrade the test strategies. Based on the fault probability model, the order of test types and test vector is being adjusted. To improve the test efficiency, the high-quality test types and test vectors are loaded first, and the fault circuits are hit earlier. A hierarchical dynamic method for IC test flow is proposed. The Bayesian probability model was established by counting the failure rates of each test type and each test vector in the sample integrated circuit, and the loading sequence of each test vector was adjusted according to the probability of hitting the fault point. As the test progresses, the test data are collected constantly, the test failure rates of test type and test vector are dynamically updated, and the loading sequence of test type and test vector is adjusted synchronously. It is proved that the final circuit test time is reduced to 32.172s by the dynamic adjustment method, and the test time is reduced by 53.9%. The use of dynamically adjusted test process can find the fault circuit earlier, significantly reduce the test time of the fault circuit, and improve the test efficiency.

Spatial matching relationship of dual heat sources in electromagnetic heating of pipes

• Dual heat sources reduce the probability of latent defects in electromagnetic heating. • The mechanism of the interference of dual heat sources on the induced magnetic field. • Reveal the spatial matching relationship of dual heat sources under moving conditions. In medium-frequency heat treatment, it is difficult to thoroughly heat the interior of welds of heavy-wall pipes, and blind zones of heating or overheating zones with latent defects are very readily present. To solve the electromagnetic-heating-induced latent defects, we researched the mode of heat transfer inside the welds with heat sources under motion conditions to determine the temperature field evolution, spatial magnetic field distribution, and spatial matching relationship in cases of dual heat sources interfering with each other, tangential to each other, and not interfering with each other. With the increase in the axial spacing between the internal and external heat sources, the magnetic field intensity at the middle of the thickest part of the welding seam on the steel pipe increased and then decreased. When the heat sources interfered with each other, the temperature rise at the weld center from electromagnetic heating was no longer fast after the Curie point was reached. The induced magnetic field heating concentration positions shifted toward both sides of the weld center. The latent defect area was reduced by 18.7 mm 2 when the spacing between the internal and external heat sources was 19 mm. This study explores the dual-heat-source interaction mechanism for the first time in the heat treatment of pipes to minimize the blind zones of heating and overheating zones.

Preliminary Design of Electromagnetic and Temperature for the DC Reactor in CRAFT Superconducting Magnet Converter System

The dc reactor is an important part of the Comprehensive Research Facility for Fusion Technology (CRAFT), which can improve the current quality and reduce the probability of faults. In this article, several common inductance calculation methods are compared, and the more effective row mutual inductance method is selected as the final calculation method of the dc reactor. Using the existing conductor size, the electromagnetic design of the reactor is carried out, and the structural size is determined. The finite-element method (FEM) is used to verify the correctness of the calculation. On the premise of the completion of the electromagnetic structure, to ensure that the temperature rise during the operation of the equipment meets the requirements, a cooling system is needed. The structure and parameters of the waterway are determined by formula calculation, and FEM is used to verify the data. This article provides a preliminary scheme for the related reactor.

Deformation behaviour of TWIP steels: Constitutive modelling informed by local and integral experimental methods used in concert

This article addresses the influence of stacking fault formation on the deformation and strain hardening behaviour of steels showing the twinning-induced plasticity effect. The role of the twin nucleation process was demonstrated on the results of mechanical testing, microstructure analyses, and constitutive modelling that were performed exemplarily for a high-alloy austenitic steel X2CrMnNi16-7-10. The microstructural evolution of the steel was studied in situ by acoustic emission measurements and post mortem by X-ray diffraction, and by scanning and transmission electron microscopy. The acoustic emission data provided information about the movement of partial and perfect dislocations and about the onset and extent of the deformation-induced twinning. The X-ray diffraction quantified lattice strains caused by the microstructure defects, and revealed averaged dislocation densities and stacking fault probabilities. The scanning electron microscopy delivered grain sizes and dislocation densities and unravelled the relationship between local grain orientation and the governing deformation mechanism. Transmission electron microscopy confirmed the validity of the microstructure model that was used for evaluation of the X-ray diffraction data, and visualized the interplay between individual deformation mechanisms. For the description of the mechanical response of the TWIP steel, a constitutive model was formulated, and supplied with the entirety of the experimental data. This approach helped to reduce the number of the free parameters of the constitutive model. Even with less free parameters, the model was able to describe the strain hardening of the steel reliably. It was found that, in contrast to dislocation motion, deformation twinning contributes to strain only marginally. The major impact of twinning on the enhanced plasticity arises from the reduction of the dislocation mean free path and the associated dislocation-related strain hardening. Based on the experimental data and model predictions, general guidelines for austenitic steel design were established. • Stacking fault mediated twinning studied in detail using complementary methods. • Quantification of the deformation mechanisms by acoustic emission measurement • The amount of twin shear itself contributes only slightly to the overall plasticity. • Modelling reveals: Increased strain hardening due to dislocation multiplication

On the enhanced hardening ability and plasticity mechanisms in a novel Mn-added CoCrNi medium entropy alloy during high-pressure torsion

Microstructure and texture evolution during high-pressure torsion (HPT) of a novel Mn-added CoCrNi medium entropy alloy (Co33Ni33Cr19Mn15) is investigated for the first time. The alloy exhibited a rapid rise in hardness at relatively low shear strains (γ≤20). It is attributed to an extensive dislocation activity to achieve saturation in dislocation density of ~1016 m−2, combined TWIP and TRIP effects and microstructural refinement. At higher shear strain, hardness increased at much reduced rates owing to saturation of dislocation density, twin fault probability and the TRIP effect, besides continued grain refinement for severe nano-structuring led to subsequent strengthening. The FCC phase showed remarkable stability except a small degree of initial deformation-induced HCP martensitic transformation in an early stage of HPT. The ideal shear texture components were observed at low shear strain, and these continued to evolve up to 5 turns of HPT processing. For similar HPT processing conditions, the studied alloy showed superior hardness (~650 Hv) compared to a wide spectrum of FCC materials, which is ascribed to a combination of the strengthening mechanisms of Taylor hardening, the TRIP and TWIP effects and Hall-Petch strengthening resulting from the nano-structured grains having an average size of ~35 nm.