Fault Probability Research Articles

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.

Advanced uncertainty based approach for discovering erasable product patterns

It is uncertain whether products will be manufactured as defective products. Producing the defective products causes the waste of time and finance in the industrial fields. When the financial crisis occurs in the manufacturing plants, production lines of the less profitable products should be stopped. To overcome the financial crisis, it is required to find the production lines that manufacture the less profitable commodities and are likely to produce the faulty merchandises and remove the corresponding production lines in order to increase the profit. Erasable pattern mining has been suggested to analyze and solve the financial problems by discovering patterns with low profit. As databases become more diverse due to the environment and characteristics of data, demands for erasable pattern mining that processes the various databases increase. An uncertain database, which is one of diverse types of databases, includes the probability related to the item, such as the probability of existence and the probability of defects. Erasable pattern mining, which considers the probability of each item, can discover pattern results which are more suitable for purpose of users. Motivated by this, we propose an efficient tree-based erasable pattern mining algorithm from the uncertain database. Considering the probability of the item, the suggested algorithm extracts erasable patterns with low profits from the uncertain database. Since both profit of the product and the probability of the item are considered, the discovered patterns are more meaningful than the patterns found by the existing erasable pattern mining. Moreover, the proposed method improves the mining operations by utilizing a list structure as well as a tree structure. We performed a performance evaluation on diverse real and synthetic datasets in order to prove the proposed algorithm outperforms the existing erasable pattern mining algorithms. The differences in runtime, memory usage, and scalability are shown through the comparison of performance with other state-of-the-art algorithms and the suggested algorithm.

Estimation of weld defects size distributions, rates and probability of detections in fabrication yards using a Bayesian theorem approach

Estimation of probability detection curves for non-destructive evaluation (NDE) typically involves the manufacturing of a high number of defect specimens followed by trial NDE and statistical analysis of the data based on the hit/miss approach. This is a time-consuming and costly procedure. Besides, probability of detection (POD) depends on a number of variables, such as human factors (operator), and the testing environment, resulting in a significant mismatch between those POD curves generated in the lab and those in practice.One application of POD curves is in the quality control of welded joints [1]. Weld quality is often characterised by the number of defects found and their size which is, inevitably, dependent on the POD of the employed NDE. Therefore, a predefined generic POD curve has certain limitations.In this paper, a method of estimating POD curves based on the Bayesian theorem of conditional probability is presented and its applicability is validated by studying an existing database under both Bayesian and the hit/miss methods. Overall, the POD predicted by the Bayesian theorem is found to be consistent with the commonly used hit/miss model. Finally, the Bayesian model is used to estimate the POD, and the true weld defect size and frequency in two ship manufacturing yards. The estimated weld defect size and frequency models provide valuable information to estimate the fatigue and fracture reliability of ship and offshore structures. It is shown that one of the yards has both better weld quality production and superior NDE detection. This will have a valuable benefit for weld quality control (QC) programmes through saving the testing resources.

Identifying and evaluating human errors in Operating a Furnace in a Petrochemical Industry Using Human Event Analysis Technique (ATHEANA)

Introduction: Industrial accidents are among the major problems of today's societies. Studies on accidents indicated that the most important cause of their occurrence was the human factor. Therefore, the purpose of this study was to identify and evaluate human error in the operation of furnace using the human event analysis technique in a petrochemical industry.&#x0D; Methods: This was a qualitative and cross-sectional study which was analyzed by directly observing the activities, interviewing the analysis, and reviewing the documents and instructions, tasks and sub-tasks by Hierarchical task analysis (HTA) method, and the results were provided in HTA charts. In the next step, according to Technique for Human Error Analysis (ATHEANA) instructions, the relevant worksheet was completed.&#x0D; Results: The results indicated that coil breakage or precision instrument connections, improper bypassing of pressure in the paths to rotate the furnace to the atmosphere, or process and leakage of gas lines, flanges and valves whose probability of defect was 0.20, 0.14 and 0.13, respectively, had the highest probability of error. The probability of human error in the operation of the furnace was calculated to be 0.63.&#x0D; Conclusion: Applying corrective measures such as: selecting skilled people, holding training courses, routine monitoring of temperature, pressure measuring equipment, etc. for proper operation of the apparatus and visual monitoring of all furnace parts, including valves, are some of the things with which the likelihood of human error in the operation of the furnaces was reduced.

Bayesian and machine learning-based fault detection and diagnostics for marine applications

ABSTRACT Marine maintenance can improve ship performance by leveraging predictive maintenance, Machine Learning and Data Analytics. This paper aims to enrich the literature, by developing a novel framework for ship diagnostics based on operational data and the probability of faults. Moreover, the framework can identify the root cause of developing faults avoiding black-box Neural Networks, and complex physics-based models. This research integrates Machine Learning-based Fault Detection, Exponentially Weighted Moving Average control charts, and Bayesian diagnostic networks which allow the examination of the rate of development (fault profile) of faults and failure modes. For validation, the case study of a marine Main Engine is used to examine faults in the engine’s Air Cooler and Air and Gas Handling System. It is concluded that any simultaneous abnormal deviations in the Main Engine’s Exhaust Gas Temperature are more likely to be caused by a fault in the Air and Gas Handling System.

Probabilistic risk assessment of radiotherapy application

The recent rapid development and increasing complexity of radiotherapy devices and applications has increased the importance of correct and safe treatment. Risk management is very important in radiotherapy (RT), because incorrect treatment can have serious consequences in terms of mortality or morbidity. However, there are currently few studies on risk analysis in RT. This quantitative and qualitative study of the radiotherapy system (all radiotherapy process) uses the fault tree method, one of the probabilistic risk assessment methods in radiotherapy applications, which is used to devise accident preventive actions. First of all, RT applications were divided into simulation, treatment planning and treatment delivery. For each, work flow charts were determined, and fault trees were created in SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations) software. Fault probabilities were determined using the expert judgment method. This analysis allowed the identification of the weak points of the system, both qualitatively and quantitatively. The analyzes also revealed that there was a 0.5% occurrence probability of a top event, determined as an incorrect dose or dose distribution in RT. It was determined that the greatest contribution to this probability value was matching error with image guidance, 7.88%. Fault tree analysis (FTA) was found to facilitate a detailed examination of the radiotherapy system. After the risk analysis, the appropriate quality control method for weak points should be determined and implemented for safety management in radiotherapy.

Diagnostics of the control system faults for automatic loading of technological machines

The structures and functions of the system for diagnosing faults in the control system for automatic loading of technological machines are described. As an example, the processor reset circuit of the control system for the automatic loading is shown. For diagnostics, the fault tree method is used. The reference book is compounded and the software diagnostics method is given. In accordance with the probability of the end of the event, the probability of processor fault and the importance of the lower events of the fault tree are determined. Keywords automatic loading, fault diagnostics, fault tree

Pyrometry-based closed-loop control of the melt pool temperature in Laser Metal Deposition with coaxial wire feeding

Laser metal deposition with coaxial wire feeding is a directed energy deposition process that enables near-net-shape and direction-independent additive manufacturing of metal parts. However, the high sensitivity of the process to disturbances poses a major challenge, as this frequently leads to defective components and rejects. To counteract this, a pyrometer was coaxially coupled into the beam path of the laser processing head to accurately determine the melt pool temperature without limiting accessibility. Applying deterministic test signals, a system model describing the dynamic relationship between the temperature and the laser power was identified. Based on this model, a closed-loop process control was designed. The control system was validated by introducing various disturbances, for which the reference temperature could be tracked with good approximation. The designed temperature control results in reduced effort for tuning the deposition process as well as a lower probability of defects, which saves manufacturing time and cost.