Defect Tracking Research Articles

A Comprehensive Overview of Manufacturing and In-service Defects in Rails: Occurrence and Remedies

Abstract: Indian Railways, the second-largest rail network in Asia and the fourth-largest in the world, has been a cornerstone of India's progress for 162 years. With over 19,000 trains serving 7,112 stations, this national institution is vital to the country’s economic and social development. It strengthens regional connectivity, enhances citizen mobility, and fuels commercial growth, all while providing top-tier logistics and transportation infrastructure. In 2021, Indian Railways freight revenues were $16 billion. Furthermore, the auto sector favours Indian Railways as its primary transporter. Although using trains for transportation has many benefits and is safe, the frequency of train accidents is continually rising. Indian Railways reported 373 train derailments between 2009 and 2015. More than 60% rail accidents are due to defects in the rail tracks. Derailment has been responsible for more than 50% of railway accidents in the past three years. Defective track, mostly rail, has been the cause of 29% of these occurrences. In the past three years, derailment-related accidents have increased by 67%. This paper discusses the different types of in-service defects in rail steels, which are the causes of accidents in India. The study also intends to identify ways of reducing the disastrous effects of vehicle derailments through suitable measures according to the gravity of railway accidents. The role of metallurgy in development of materials for rails is also highlighted.

Fault Detection Using Artificial Neural Network and Wavelet Transforms for Power Transformer

Abstract: The requirement for a stable supply of electrical energy to meet the needs of the modern world has expanded dramatically, necessitating near-faultless power system functioning. The main goal is to reduce the frequency and duration of undesired power transformer outages by imposing a high point demand that includes criteria for dependability (no false tripping) and operating speed (quick fault detection and clearance time). For many years, the second harmonic restraint concept has been widely applied in industrial applications. It employs the discrete Fourier transform (DFT) and frequently confronts issues like lengthy restrain times and the inability to distinguish internal defects from magnetizing inrush circumstances. As a result, artificial neural networks (ANNs), a strong tool for artificial intelligence (AI) that can imitate and automate information, have been suggested for defect identification and tracking in normal and inrush conditions. For the investigation of power transformer transient conditions under diverse settings, the wavelet transform (WT) is utilized, which has the capacity to extract information from transient signals in both the time and frequency domains at the same time. In the MATLAB/SIMULINK environment, all of the above-mentioned conditions of a power transformer to be investigated in a power system are modelled.

How do software practitioners perceive human-centric defects?

Context:Human-centric software design and development prioritises the way users prefer to complete their jobs, rather than expecting users to adapt to the software. Software users can have different genders, ages, cultures, languages, disabilities, socioeconomic statuses, and educational backgrounds, among many other differences. Due to the inherently varied nature of these differences and their impact on software usage, preferences and issues of users can vary, resulting in user-specific defects that we term as ‘human-centric defects’ (HCDs). Objective:This research aims to understand the perception and current management practices of such HCDs by software practitioners, identify key challenges in reporting, understanding and fixing them, and provide recommendations to improve HCDs management in software engineering. Methods:We conducted a survey and interviews with software engineering practitioners to gauge their knowledge and experience on HCDs and the defect tracking process. Results:We analysed fifty (50) survey- and ten (10) interview-responses from SE practitioners and identified that there are multiple gaps in the current management of HCDs in software engineering practice. There is a lack of awareness regarding human-centric aspects, causing them to be lost or under-appreciated during software development. Our results revealed that handling HCDs could be improved by following a better feedback process with end-users, a more descriptive taxonomy, and suitable automation. Conclusion:HCDs, given their diverse end-user base, present a major challenge to software practitioners. In the software engineering domain, research on HCDs has been limited and requires effort from research and practice communities to create awareness and support for human-centric aspects.

Bug Tracking System

For many years, bug-tracking mechanisms have been employed only in some of the large software development houses. Most of the others never bothered with bug tracking at all, and instead simply relied on shared lists and email to monitor the status of defects. This procedure is error-prone and tends to cause those bugs judged least significant by developers to be dropped or ignored. Bug Tracking System is an ideal solution to track the bugs of a product, solution or an application. Bug Tracking System allows individuals or groups of developers to keep track of outstanding bugs in their product effectively. This can also be called the Defect Tracking System. The Bug Tracking System can dramatically increase the productivity and accountability of individual employees by providing a documented workflow and positive feedback for good performance. Moreover, the project explores the use of machine learning for automated root cause analysis, assisting developers in identifying underlying issues contributing to bug occurrences. Recommendation systems are also developed to suggest potential fixes or solutions for reported bugs based on past incidents and code repositories, aiding developers in efficient bug resolution. Through these ML-driven functionalities, the proposed bug tracking software aims to revolutionize the bug management process, enabling faster bug detection, prioritization, and resolution, thereby enhancing software quality, user satisfaction, and overall development productivity. Key Words: Bug Tacking system, apps, users.

IMPLEMENTING VIRTUAL REALITY TO FOSTER CONSTRUCTION DEFECTS CHECKING AND REPORTING

The scarce human workforce during the economic downturn and the COVID lockdown measures exposed the need for more reliance on in-person construction site visits. During the pandemic, the research team conducted a systematic review to investigate how virtual reality (VR) techniques can be applied to foster more effective construction defect checking and reporting. The findings indicate that VR technologies can be applied in marker-based inspection, which is crucial for developing the automatic defect recording and tracking system. Built on these findings, this paper reports a case study conducted in Melbourne, Australia, demonstrating how VR techniques can be applied to develop practical solutions for enhancing defect checking and reporting in an actual construction project. The solutions focused on reducing the time-consuming and inefficient manual tracking process. This study shows a step forward in assisting human judgment by resolving the in-person marker-based inspections and manual defect tracking encountered in traditional defect checking and reporting practices.

IMPLEMENTING VIRTUAL REALITY TO FOSTER CONSTRUCTION DEFECTS CHECKING AND REPORTING

The scarce human workforce during the economic downturn and the COVID lockdown measures exposed the need for more reliance on in-person construction site visits. During the pandemic, the research team conducted a systematic review to investigate how virtual reality (VR) techniques can be applied to foster more effective construction defect checking and reporting. The findings indicate that VR technologies can be applied in marker-based inspection, which is crucial for developing the automatic defect recording and tracking system. Built on these findings, this paper reports a case study conducted in Melbourne, Australia, demonstrating how VR techniques can be applied to develop practical solutions for enhancing defect checking and reporting in an actual construction project. The solutions focused on reducing the time-consuming and inefficient manual tracking process. This study shows a step forward in assisting human judgment by resolving the in-person marker-based inspections and manual defect tracking encountered in traditional defect checking and reporting practices.

Dynamic Defect Detection in Fast, Robust NDE Methods by Transfer Learning Based Optimally Binned Hypothesis Tests

ABSTRACT The structural integrity of safety-critical infrastructures diminishes over time, necessitating periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) readings for defect detection can be both time-consuming and costly. In practical applications, NDE data can be highly noisy due to factors like liftoff/stand-off distances, probe drift, scanning speed, and variations in data acquisition rates. Given the broad spectrum of potential uncertainties that can contribute to noise accumulation, characterizing the noise in such NDE data presents significant difficulties. As a result, the extraction of defect signals through deconvolution becomes challenging. This article introduces an NDE-based dynamic defect tracking framework designed for robust conditions. It incorporates regular periodic monitoring of the material under test (MUT) using a cost-effective, easy-to-implement magnetic flux leakage (MFL) probe. The framework proposes a dynamic setup that includes occasional, highly accurate, expensive MFL scans among regular, low-cost, noisy MFL scans. The objective is to detect and precisely track defect formation in metallic pipes over time, thereby enabling the efficient alarming and localization of defects before they reach detrimental sizes. A key characteristic of the proposed dynamic monitoring method is its noise type agnosticism in the low-cost MFL scans. It remains effective even when the underlying noise-producing uncertainties fluctuate significantly over time, a feature achieved through the use of transfer learning. The method transfers accurate information regarding the locations and sizes of existing corrosions from the detailed MFL scan to the subsequent noisy MFL inspections. With this supportive information from defective scan-points, it estimates the degradation of the signal-to-noise ratio (SNR) in the noisy MFL scans. The framework suggests binned hypothesis tests in noisy MFL scans, setting the level of aggregation based on the SNR estimate in the scan data. This proposed binned hypothesis test optimizes defect coverage and maintains control over the false discovery rate (FDR). Experimental MFL data, gathered under a wide range of uncertainties and defect types, illustrates the highly heterogeneous nature of noise distributions in low-cost MFL scans and the significant variance in SNR deteriorations. The necessity of using binned hypothesis tests in these low-cost MFL data is highlighted by the poor performance of pointwise multiple hypothesis tests. Finally, the high effectiveness of the proposed transfer learning-based binned hypothesis testing method is demonstrated. Although the method was applied to MFL data in this instance, it is also suitable for other NDE applications. The applicability of the algorithms has also been confirmed through validation on experimentally generated Eddy Current (EC) data.

Tracking of structural defects induced by Eu-doping in β-Ag2MoO4: their influences on electrical properties.

In this study, several methods were employed to investigate the electrical characteristics of β-Ag2MoO4 systems, both Eu-doped and undoped, synthesized using the microwave-assisted hydrothermal method. The focus extended to understanding how synthesis time influences material defects, with doping fixed at 1%. A systematic shift in the silver vacancy (VAg) concentration was observed within the doped β-Ag2MoO4 system. Specifically, this study demonstrated that the incorporation of Eu3+ into polycrystalline β-Ag2MoO4 initially increases the VAg concentration. However, as the synthesis time progresses, the VAg concentration decreases, resulting in alterations in the resulting electrical properties, arising from the intricate interplay between the number of grain boundaries and carrier density. By combining information obtained from photoluminescence, positron annihilation lifetime spectroscopy, and impedance spectroscopy, a comprehensive conduction mechanism was formulated, shedding light on both doped and undoped β-Ag2MoO4 systems.

Handling uncertainty issue in software defect prediction utilizing a hybrid of ANFIS and turbulent flow of water optimization algorithm

During the development cycle of software projects, numerous defects and challenges have been identified, leading to prolonged project durations and escalated costs. As a result, both product delivery and defect tracking have become increasingly complex, expensive, and time-consuming. Recognizing the challenge of identifying every software defect, it is crucial to foresee potential consequences and strive for the production of high-quality products. The goal of software defect prediction (SDP) is to identify problematic locations within software code. This study presents the first experimental investigation utilizing the turbulent flow of water optimization (TFWO) in conjunction with the adaptive neuro-fuzzy inference system (ANFIS) to enhance SDP. The TFWO_ANFIS model is designed to address the uncertainties present in software features and predict defects with feasible accuracy. Data are divided randomly at the beginning of the model into training and testing sets to avoid the local optima and over-fitting issues. By applying the TFWO approach, it adjusts the ANFIS parameters during the SDP process. The proposed model, TFWO_ANFIS, outperforms other optimization algorithms commonly used in SDP, such as particle swarm optimization (PSO), gray wolf optimization (GWO), differential evolution (DE), ant colony optimization (ACO), standard ANFIS, and genetic algorithm (GA). This superiority is demonstrated through various evaluation metrics for four datasets, including standard deviation (SD) scores (0.3307, 0.2885, 0.3205, and 0.2929), mean square error (MSE) scores (0.1091, 0.0770, 0.1026, and 0.0850), root-mean-square error (RMSE) scores (0.3303, 0.2776, 0.3203, and 0.2926), mean bias error (MBE) scores (0.1281, 0.0860, 0.0931, and 0.2310), and accuracy scores (87.3%, 90.2%, 85.8%, and 89.2%), respectively, for the datasets KC2, PC3, KC1, and PC4. These datasets with different instances and features are obtained from an open platform called OPENML. Additionally, multiple evaluation metrics such as precision, sensitivity, confusion matrices, and specificity are employed to assess the model’s performance.

Does preoperative glenoid bony defect determine final coracoid graft positioning in arthroscopic Latarjet?

It has been demonstrated that the accurate positioning of the graft is key to restoring shoulder stability and preventing future arthrosis development. Preoperative anteroinferior glenoid bone loss is frequently encountered when performing a Latarjet, and it has not been determined yet if the amount of bony defect can influence graft positioning. The aim of the study was to determine if a preoperative glenoid bony defect has an influence on the final coracoid graft position in the arthroscopic Latarjet procedure. Fifty-five patients who underwent the arthroscopic Latarjet procedure were included, with a minimum follow-up of 2 years. There were 51 men (92.7%). Mean age was 29.1 (SD 7.63). Western Ontario Shoulder Instability Index, Rowe, and Single Assessment Numeric Evaluation scores were fulfilled. All measurements were performed by a musculoskeletal radiologist based on a multiplanar bidimensional CT scan. Dimensions of the glenoid, glenoid defect, and glenoid track were calculated. Position of the graft was evaluated in the axial (distance to glenoid surface, angulation of the graft and screws) and sagittal planes (percentage of the coracoid graft below the equator) as described by Kany etal and Barth etal respectively. There was a glenoid defect in 41 patients (74.5 %). Mean width of the defect was 4.32 mm (SD 3.08) which represented 15.3% of the native glenoid surface (SD 10.8). 78.2% of the patients were offtrack preoperatively, and 11.9% remained offtrack postoperatively. The final glenoid diameter with the graft was 32.1 mm (SD 4.34). Mean distance from the graft to the glenoid at 50% height was 1.1 mm (SD 2.19 mm) and at 25% height was 1.31 mm (SD 2.05). Mean angulation of the superior and inferior screws were 26.9° (SD 8.2°) and 27.1° (SD 7.35°), respectively. In 81.8% of the cases, the graft was deemed to be flush with the glenoid. The percentage of the coracoid graft under the equator of the glenoid was 71.2 % (SD 21.8). There was not a statistically significant difference in screw angulation or graft positioning in the axial plane when comparing patients who had a glenoid defect with those who did not, or depending on the size (P>.05). Percentage of graft below the equator was, however, lower in patients without bony defect (P=.04). This study showed that accurate position of the coracoid graft is achieved in the presence of a glenoid bony defect. In the cases of intact glenoid, the height of the graft should be carefully evaluated.

Cognitive Complexity Applied to Software Development: An Automated Procedure to Reduce the Comprehension Effort

The cognitive complexity of a software application determines the amount of human effort required to comprehend its internal logic, which results in a subjective measurement. The quantification process of the cognitive complexity as a metric is problematic since the factors representing the computation do not represent the exact human cognition. Therefore, the determination of cognitive complexity requires expansion beyond its quantification. The human comprehension effort related with a software application is associated with each phase of its development process. Correct requirements identification and accurate logical diagram generation prior to code implementation can lead to proper logical identification of software applications. Moreover, human comprehension is essential for software maintenance. Defect identification, correction and handling of code quality issues cannot be maintained without good comprehension. Therefore, cognitive complexity can be effectively applied to demonstrate human understandability inside the respective phases of requirements analysis, design, defect tracking, and code quality optimization. This study involved automation of the above-mentioned phases to reduce the manual human cognitive load and reduce cognitive complexity. It was found that the proposed system could enhance the average accuracy of requirements analysis and class diagram generation by 14.44% and 9.89% average accuracy incrementation through defect tracking and code quality issues compared to manual procedures.

An Online Rail Track Fastener Classification System Based on YOLO Models

In order to save manpower on rail track inspection, computer vision-based methodologies are developed. We propose utilizing the YOLOv4-Tiny neural network to identify track defects in real time. There are ten defects covering fasteners, rail surfaces, and sleepers from the upward and six defects about the rail waist from the sideward. The proposed real-time inspection system includes a high-performance notebook, two sports cameras, and three parallel processes. The hardware is mounted on a flat cart running at 30 km/h. The inspection results about the abnormal track components could be queried by defective type, time, and the rail hectometer stake. In the experiments, data augmentation by a Cycle Generative Adversarial Network (GAN) is used to increase the dataset. The number of images is 3800 on the upward and 967 on the sideward. Five object detection neural network models-YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, SSD512, and SSD300-were tested. The YOLOv4-Tiny model with 150 FPS is selected as the recognition kernel, as it achieved 91.7%, 92%, and 91% for the mAP, precision, and recall of the defective track components from the upward, respectively. The mAP, precision, and recall of the defective track components from the sideward are 99.16%, 96%, and 94%, respectively.

Tracking of Point Defects in the Full Compositional Range of AlGaN via Photoluminescence Spectroscopy

A comprehensive energy map as a function of AlGaN composition over the whole alloy range is presented for commonly observed point defects in nominally intrinsic, n‐, and p‐doped material. The map covers intentional and unintentional impurities (CN, MgIII), vacancies (VIII, VN), passivating complexes (H), and self‐compensating complexes. The tracking of these defects is crucial to understand their impact on optical and electrical properties as well as for their mitigation.

An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software

The resolution of a software bug depends on the severity of the defect report. Open-source software defect tracking solutions have taken over as the principal means of processing enormous amounts of defect information data due to the ongoing increase in software scale. Dealing with software faults requires analyzing the implications of defect report severity in the data warehouse. Thus, the authors have proposed an optimized systematic approach through the research and analysis of Bugzilla defect tracking system data in this study, where it is found that the attribute characteristics of different projects are quite different and the statistical features of the repair rate, resolution time, developers, components, and other attributes are consistent. This technique, therefore, assumes that a rise in the severity of software defect reports will result in a rise in the defect repair rate and that the severity is normally based on the severity distribution of various components and projects. According to the study’s findings, developers hold the most defects when the repair rate is low and the defect resolution time is shortest.

Laser processing of microdroplet structure of liquid crystal in 3D

Processing of mesoscale structures of soft matter and liquid is of great importance in both science and engineering. In this work, we introduce the concept of laser-assisted micromachining to this field and inject a certain number of microdroplets into a preselected location on the surface of a liquid crystal drop through laser irradiation. The impact of laser energy on the triggered injection is discussed. The sequentially injected microdroplets are spontaneously captured by the defect ring in the host drop and transported along this defect track as micro-cargos. By precisely manipulating the laser beam, the tailored injection of droplets is achieved, and the injected droplets self-assemble into one necklace ring within the host drop. The result provides a bottom-up approach for the in-situ and three-dimensional microfabrication of droplet structure of soft matter using a laser beam, which may be applicable in the development of optical and photonic devices.

Characterization of horseshoe-shaped defects in 4H-SiC thick homoepitaxial layers

We report horseshoe-shaped protuberance defects encountered in growing thick homoepitaxial films on 4° off c-axis 4H-silicon carbide substrates. Their morphology, polytype and formation mechanism are investigated by optical microscopy, laser microscopy, micro-Raman spectroscopy, micro-photoluminescence spectroscopy and defect tracking experiments. The defects are observed to protrude from the surrounding area with a facet of (0001) at an angle of about 3.06°. Concomitantly, surface undulations develop and extend about hundreds of microns from the defect perpendicular to the direction of step flow. No polytype inclusions are found in these defects. The defect tracking reveals that the defects are not directly related to the existence of dislocations. We think that the formation of the defects origin from the occurrence of giant step bunching and subsequent faceting.

Application of Data Mining Technology in Software Engineering

With the rapid development of informatization, computer database software systems have entered various fields of society, which has brought about the explosive growth of industry data. Faced with massive amounts of data, computers with limited storage capacity have to abandon some outdated data, and the application of various data mining technologies related to it has gradually matured. The purpose of this article is to discuss the application research of data mining technology in software engineering. This article analyzes the correlation analysis of a large number of bug repair source code update data and bug defect reports in the version control system SVN and the defect tracking system Bugzilla in the software engineering project development process, and tries to classify the bug report by data mining technology: defect changes and potential defects change. Starting from large-scale software engineering projects, apply data mining technology to the huge software engineeri ng knowledge base. Especially the software development and maintenance are explained, as well as the more challenging problems in the future. This paper uses data mining technology to study the dependency of the source code files of each module of the software system, and helps software developers quickly understand the software architecture by understanding the interrelationships between the modules, and provides suggestions for modification paths. Experimental research shows that this paper compares with F-measure and concludes that FL-M-GSpan algorithm is better than TS-M-GSpan algorithm. At the same time, it is found that the FL-M-GSpan algorithm always has a better accuracy rate close to 95%, while the TS-M-GSpan algorithm always has a better recall rate.

Deep Learning-based Computer Vision for Radiation Defect Analysis: from Static Defect Segmentation to Dynamic Defect Tracking

Deep Learning-based Computer Vision for Radiation Defect Analysis: from Static Defect Segmentation to Dynamic Defect Tracking

Ultrafast Ratchet Dynamics of Skyrmions by Defect Engineering in Materials with Poor Conductivity Under Gigahertz Magnetic Fields

The ratchet motion of magnetic skyrmions driven by microwave magnetic fields, with the motion direction and speed tunable by field parameters, provides a promising route to drive magnetic skyrmions in materials with poor conductivity. However, as an indirect motion, skyrmion ratchet-motion speed is generally low in comparison with the direct motions driven by currents. Toward practical applications, it is necessary to ask if there are mechanisms to realize ultrafast ratchet motion of magnetic skyrmions and how such a motion can be integrated into racetrack-type skyrmion devices. In this work, we explore the effects of defects and edges on the ratchet motion of magnetic skyrmions under time-varying magnetic fields in GHz. We demonstrate that the ratchet motion of skyrmion is not only guided along the defect tracks or edges, but also with a remarkable speed up (with a factor over 10) compared with that in the bulk region. The skyrmion ratchet-motion speed reaches 100 m/s along a straight defect track and edge and ${10}^{9}$ rad/s along a circular edge under a field of approximately 50 mT, comparable to those direct motions driven by currents. Moreover, the skyrmion ratchet motion along the defect track or edge can be facilely controlled by the field and defect parameters. Analysis based on the time-averaged Thiele equation of skyrmion verifies that such a speed-up effect is due to the increased time-averaged driving force perpendicular to the skyrmion motion when it approaches the defect track or edge, analogous to that discovered in direct motions driven by currents. The generality of our conclusions has been examined for the ratchet motion of Bloch and N\'eel-type skyrmions driven by a variety of time-varying magnetic fields, and for systems with open edges or defect tracks with modified Dzyaloshinskii-Moriya or exchange interactions and anisotropy.

Rapid Non-Destructive Wafer Detection and Tracking of Defects in Bulk and Epitaxially Grown Gallium Nitride

Defects in bulk semiconductor crystals are especially damaging to devices, producing catastrophic electrical and optical failures or even limiting lifetime performance and reliability, while driving materials growth costs. New methods for rapid and non-destructive characterization of materials are needed to help overcome limitations in fabrication throughput and application reliability of wide-bandgap power electronics substrates, especially in the development of emerging bulk Gallium Nitride (GaN). Rapid materials characterization methods will help inform the fabrication process parameters impact on materials defects formation, while speeding-up materials development and, ultimately, the deployment of low defect commercially viable and reliable bulk GaN substrates. Here we present an approach for multi-modal defect non-destructive imaging of device-relevant GaN defects with high resolution and high sensitivity. The scanning GaN defects detection system is based on laser pump-and-probe photoluminescence and photothermal measurements that are compared to diode device reliability data from accelerated lifetime testing. We determine that defects detected at optical frequencies reliably predict lifetime performance issues of power electronic devices operating at near DC frequencies[1, 2]. Bulk and epi grown GaN wafer imaging data and test platform device data are correlated to help validate the proposed multi-modal defect detection approach for the reliable detection of defects relevant to the performance of GaN power electronic devices.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office, FY18/FY19 Lab Call. J. H. Yoo, A. Lange, J. Chesser, S. Falabella, and S. Elhadj, "A Survey of Transparent Conducting Films and Optoelectrical Materials for High Optical Power Applications," Physica Status Solidi a-Applications and Materials Science 216, (2019).J.-H. Yoo, S. Rafique, A. Lange, H. Zhao, and S. Elhadj, "Lifetime laser damage performance of β-Ga2O3 for high power applications," APL Materials 6, 036105 (2018). Figure 1