Wheel tread defect detection for high-speed trains using FBG-based online monitoring techniques

Liu, Xiao-Zhou,Ni, Yi-Qing Techno-Press 2018 Smart Structures and Systems, An International Jou Vol.21 No.5

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

공동주택 하자유형별・공종별・공간별 실태분석 연구-하자심사분쟁조정위원회 신청건수를 중심으로

염태준,부윤섭,최경철,서동구,황은경 한국건축시공학회 2024 한국건축시공학회지 Vol.24 No.6

본 연구에서는 하자신청 데이터 분석과 하자관련 법령 분석을 통해 향후 하자예방 지침 개발을 목적으로 한 기초연구이 다. 하자정보시스템에 신청된 하자신청 건수 총 70,687건을 활용하였으며 하자종류, 공종, 하자공간으로 구분하여 데이터 를 분석하였다. 데이터 분석결과 결로, 누수, 균열, 오염 및 변색, 들뜸 및 탈락, 미시공, 파손, 소음이 전체 하자의 66%를 차지 하는 주요하자로 도출되었다. 공종에서는 마감공사와 창호공사에서 50% 이상의 하자가 발생하였으며 공간에서는 침실, 거 실, 화장실이 높은 비율을 차지하는 것으로 분석되었다. This study serves as a foundational research aimed at developing future defect prevention guidelines through an analysis of defect application data and related legal regulations. A total of 70,687 defect claims submitted to the Defect Information System were utilized, and the data was analyzed by defect type, construction work, and defect space. The analysis results revealed that condensation, leakage, cracks, staining and discoloration, peeling and detachment, incomplete construction, damage, and noise accounted for 66% of all defects. In construction work, over 50% of defects occurred in finishing work and window and door work, while bedrooms, living rooms, and bathrooms showed the highest rates of defects in spatial terms.

Identification of the Vital Causes of Defects in a Dyeing Textile Industry

Fazila Munir, Nadia Saeed,Moustafa Omar Ahmed Abu-Shawie 대한산업공학회 2024 Industrial Engineeering & Management Systems Vol.23 No.3

The study aims to identify causes of defects in textile industry through a comprehensive analysis of manufacturing processes and associated factors. This applied research, based on the defect data collected from a textile industry of Lahore, Pakistan, aims to articulate and resolve the defect causes of that industry. The interesting findings are also helpful for other textile manufacturers. The six-sigma DMAIC (Define, Measure, Analyze, Improve and Control) roadmap based on five phases used to identify root causes of defects associated with textile industry. The research reveals common types of defects, their distribution across production stages and primary causes contributing to their occurrence. The five phase of DMAIC cycle extracted the significant defect contributors. The analysis shows that out of 61 selected lots, the slub (Max value=45; Mode=32) and stain (Max value=95; Mode=36) are most frequently occurring types of defects. For the statistical implication, factor analysis is applied to identify the major factors causing defects in the dyeing process. Based on the highest factor loadings, three main factors are identified: F1 (Material Defects), F2 (Fabric Imperfections), and F3 (Yarn Defects).The research addresses the causes, including regular machinery maintenance, employee training, quality control improvements and enhanced communication channels. Implementing these measures can help textile manufacturers to improve product quality, customer’s satisfaction, and overall business performance. This research makes a novel contribution to the identification and problem-solving in textile manufacturing industries.

유지관리단계의 하자 재발생을 고려한 창호공사 시공단계의 중점관리요소 분석

정우진 ( Jeong U Jin ),김대영 ( Kim Dae Young ),임지영 ( Lim Jeeyoung ),박현정 ( Park Hyun Jung ) 한국건축시공학회 2021 한국건축시공학회지 Vol.21 No.6

As the construction standards for energy-saving eco-friendly housing have recently been strengthened, the proportion of window work has increased with the demand for high-efficiency housing. Windows have high frequency of use, and there is the potential for many defects to occur depending on the characteristics of construction. According to a government agency’s survey of defects in public rental apartment housing, defects in the windows work accounted for the highest portion of complaints received. Accordingly, related previous studies were considered, and it was found that the existing studies in Korea lacked research that reflected the construction characteristics of window work and the importance of maintenance. In addition, existing overseas studies considered both the constructor and the resident's position, considering the cost aspect together, and showed a trend of structuring the relationship between defects and causes. Therefore, this study will analyze the causes of defects that can occur in the construction phase of the windows work, reflect the construction characteristics, and derive major factors that consider the importance of maintenance based on the possibility of recurrence after repairing defects. Ultimately, this research will contribute to preventing defects in the construction phase and reducing maintenance costs by presenting a highly effective defect management plan through selecting the major factors for each defect type that can be intuitively judged by analyzing the causal relationship between defect types and causes.

Defect Modelling and Tool Selection for Ultrasonic Machining Process Using Finite Element Analysis

Mehdi Mehtab Mirad,Bipul Das 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.24 No.2

The machining performance of an ultrasonic machine mainly depends on the ability of the design of ultrasonic tool. A tool is a significant component in the ultrasonic machining process that contacts the abrasive particles to remove the material from the workpiece. The present investigation has considered the design of three different tool profiles as cylindrical, conical and stepped. A defect is introduced in three different orientations namely longitudinal, perpendicular and inclined about the vibration axis in all the respective tool profiles. The effect of vibration frequency during the machining on the defective and non-defective tools is analysed using numerical simulation technique. The study also presents the modal analysis to obtain the mode shapes and natural frequencies of the tool profiles with and without the defect. The induced stress is computed by performing the harmonic analysis for the defective and non-defective tools. Out of the three profiles analysed in this investigation, the conical tool profile without defect results in a maximum stress of 133 MPa and the same in presence of the internal defect in the inclined orientation is 537 MPa, which is 35% higher than the ultimate tensile strength of the tool material. The comparison of the tool profiles demonstrates that the stepped tool results in maximum Eigen frequency of 134.24 kHz with maximum stress of 453 MPa with the defect in the inclined axis at 30 kHz excitation frequency.

Grad-CAM을 활용한 EfficientNetV2 기반 웨이퍼 맵 불량분석 연구

이한성,조현종 대한전기학회 2023 전기학회논문지 Vol.72 No.4

In semiconductors, various defect patterns appear on the wafer map due to problems in the design and manufacturing process. Analysis of generated defect patterns will reduce the rate of defects and enable the production of high-quality semiconductors. Considering the number of semiconductors produced, performing defect analysis by human resources is inefficient. Recently, as hardware performance has improved, high-performance deep learning models have been designed. These models show high performance in image classification and have advantages in terms of processing speed. Therefore, this paper used EfficientNetV2 designed to achieve maximum efficiency with few parameters for semiconductor failure analysis. Identifying the location of defects is a critical element of defect analysis, not just classifying defect patterns. Therefore, in this study, we used Grad-CAM to identify the classification of defect patterns and their approximate location. Wafer map dataset is difficult to collect as data includes defects in manufacturing companies' processes. To train EfficientNetV2, we used the WM-811K dataset, a publicly available dataset on Kaggle. This dataset has an imbalance in the number of data between classes. We increased the data using Flip and Rotate to address the dataset's imbalances, ultimately improving the classification performance. The test results showed an accuracy of 0.944 and an F1-score of 0.929.

명암도 분포 및 형태 분석을 이용한 효과적인 TFT-LCD 필름 결함 영상 분류 기법

노충호(Chung-Ho Noh),이석룡(Seok-Lyong Lee),조문신(Moon-Shin Zo) 한국멀티미디어학회 2010 멀티미디어학회논문지 Vol.13 No.8

TFT-LCD 생산 과정에서 발생하는 결함을 정확하게 분류하여 결함 유형에 따라 폐기, 사용가능 등의 의사결정을 적절하게 내리는 것은 수율 증가 및 생산성 향상에 필수적인 요소이다. 본 논문에서는 TFT-LCD 생산 라인에서 획득한 결함 영상에 대하여 명암도 분포(intensity distribution) 및 결함 영상의 형태 특징(shape feature)을 분석하여 효과적으로 필름 결함 유형을 분류하는 기법을 제시한다. 본 연구에서는 먼저 필름 결함 영상을 결함 영역과 결함이 아닌 배경 영역으로 이진화하고, 결함 영역에서 결함의 선형성(linearity), 명암도 분포를 고려한 형태 특징 등의 여러 가지 특징을 분석하여 기준 영상(referential image) 데이터베이스를 구축하였으며, 분류하고자 하는 결함 영상과 데이터베이스에 저장된 기준 영상과의 매칭 비용 함수(matching cost function)를 정의하여 적절히 매칭시킴으로써 결함의 유형을 결정하였다. 제시한 기법의 성능을 검증하기 위하여 실제 TFT-LCD 생산 라인에서 획득한 결함 영상들을 대상으로 분류 실험을 수행하였으며, 실험 결과 생산 라인에서 이용할 수 있을 정도의 상당한 수준의 분류 정확도를 달성하였음을 보여주었다. In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity. and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

텍스트 마이닝 기법을 이용한 공동주택 하자의 유지관리 요소 분석

방홍순,허한결,김옥규 대한건축학회 2022 대한건축학회논문집 Vol.38 No.9

There are secondary defects in apartment buildings that are not directly produced from construction errors, but are derived from other types of defects. To improve the quality of the type of construction that causes these secondary defects, it is important to define and manage them by analyzing data regarding defects in apartment buildings. This study analyzed approximately 1.33 million cases that finished defect repairs in 2019 then defined the frequency and repair costs of fundamental and secondary defects. This analysis revealed that secondary defects had accounted for 3.83% of the entire defect frequency and made up 6.08% of the repair costs. Using text mining techniques, this analysis presented the types of construction that had caused secondary defects in apartment buildings in the order of frequency and average similarity to reduce such secondary defects. According to the similarity analysis of the types of construction, wallpaper work topped the frequency order with 68,060 cases, which took up 38.36%, and its similarity was 0.615 on average. Furthermore, with 4,518 cases, electricity work was first in the average frequency at 2.54% and its average similarity was 0.955. To present these results as a whole, the word cloud technique was used. 공동주택 하자의 일부는 해당공종 자체에서 발생한 하자가 아닌 다른 하자에 유발되어 발생한 2차 하자가 존재한다. 이를 해결하기 위해서는 공동주택 하자 데이터 분석을 통하여 2차 하자를 유발하는 공종을 도출하여 관리하는 것은 중요하다. 이에 본 연구에서는 2019년에 하자보수가 완료된 약 133만 건의 데이터 분석을 통하여, 1차 하자와 2차 하자 빈도 및 보수비용을 도출하였다. 2차 하자는 전체 하자 중 3.83%의 빈도를 가지고 있었으며, 6.08%의 보수비용을 차지하고 있었다. 이를 저감하기 위해 텍스트 마이닝 기법을 분석하여 공동주택에서 발생하는 2차 하자의 유발 공종을 개수 순서와 유사도 평균 순서를 도출하였다. 유발공종 유사도 분석 결과, 개수 순번으로 도배공사(68,060건, 38.36%)로 가장 높았으며, 유사도 평균 0.615로 도출되었다. 평균 순번으로 일반전기(4,518건, 2.54%)로 가장 높았으며, 유사도 평균 0.955로 도출되었다. 이를 전체적으로 표현하기 위해 워드 클라우드 시각화 기법을 통하여 도출하였다.

Fatigue life evaluation of socket welded pipe with incomplete penetration defect: I-test and FE analysis

Lee, Dong-Min,Kim, Seung-Jae,Lee, Hyun-Jae,Kim, Yun-Jae Korean Nuclear Society 2021 Nuclear Engineering and Technology Vol.53 No.11

This paper presents experimental and numerical analysis results regarding the effects of an incomplete penetration defect on the fatigue lives of socket welded pipes. For the experiment, four-point bending fatigue tests with various defect geometries (defect depth and circumferential length) were performed, and test results are presented in terms of stress-life data. The results showed that for circumferentially short defects, the fatigue life tends to increase with increasing crack depth, but for longer defects, the trend becomes the opposite. Finite element analysis showed that for short defects, the maximum principal stress decreases with increases in crack depth. For a longer defect, the opposite trend was found. Furthermore, the maximum principal stress tends to increase with an increase in defect length regardless of the defect depth.

Influence of vacancy defects on vibration analysis of graphene sheets applying isogeometric method: Molecular and continuum approaches

Vahid Tahouneh,Mohammad Hasan Naei,Mahmoud Mosavi Mashhadi 국제구조공학회 2020 Steel and Composite Structures, An International J Vol.34 No.2

The main objective of this research paper is to consider vibration analysis of vacancy defected graphene sheet as a nonisotropic structure via molecular dynamic and continuum approaches. The influence of structural defects on the vibration of graphene sheets is considered by applying the mechanical properties of defected graphene sheets. Molecular dynamic simulations have been performed to estimate the mechanical properties of graphene as a nonisotropic structure with single- and double- vacancy defects using open source well-known software i.e., large-scale atomic/molecular massively parallel simulator (LAMMPS). The interactions between the carbon atoms are modelled using Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential. An isogeometric analysis (IGA) based upon non-uniform rational B-spline (NURBS) is employed for approximation of single-layered graphene sheets deflection field and the governing equations are derived using nonlocal elasticity theory. The dependence of small-scale effects, chirality and different defect types on vibrational characteristic of graphene sheets is investigated in this comprehensive research work. In addition, numerical results are validated and compared with those achieved using other analysis, where an excellent agreement is found. The interesting results indicate that increasing the number of missing atoms can lead to decrease the natural frequencies of graphene sheets. It is seen that the degree of the detrimental effects differ with defect type. The Young’s and shear modulus of the graphene with SV defects are much smaller than graphene with DV defects. It is also observed that Single Vacancy (SV) clusters cause more reduction in the natural frequencies of SLGS than Double Vacancy (DV) clusters. The effectiveness and the accuracy of the present IGA approach have been demonstrated and it is shown that the IGA is efficient, robust and accurate in terms of nanoplate problems.