• Title/Summary/Keyword: Failure pattern

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Comparison of Electrical Tree Initiation According to Flow Pattern in EHV Power Cable Insulation (초고압 전력 케이블 절연층의 Flow Pattern 방향각에 따른 전기 트리 개시 특성 비교)

  • Lee, Seung-Yoo;Kim, Young-Ho;Cho, Dae-Hee;Lee, In-Ho;Park, Wan-Ki
    • Proceedings of the KIEE Conference
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    • 1999.07d
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    • pp.1532-1534
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    • 1999
  • Electrical treeing phenomenon, regarded as pre-breakdown which accelerates aging process leading an insulation to the complete breakdown, is with no doubt extremely fatal to the performance of the insulation. Investigated in this paper is electrical treeing representing local dielectric failure according to flow pattern, the flow history of liquid polyethylene formed during the extrusion process. Experiments of electrical tree initiation by means of ramp tests were conducted using newly developed electrode system with point-to-point structure. Constant voltage tests were also carried out with the electrode system to estimate the life time of the insulation. Results were analyzed using statistical method such as Weibull distribution.

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Parity Space and Pattern Recognition Approach for Hardware Redundant System Signal Validation using Artificial Neural Networks (인공신경망을 이용하여 하드웨어 다중 센서 신호 검증을 위한 패리티 공간 및 패턴인식 방법)

  • 윤태섭
    • Journal of Institute of Control, Robotics and Systems
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    • v.4 no.6
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    • pp.765-771
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    • 1998
  • An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.

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Pattern Recognition of Rotor Fault Signal Using Bidden Markov Model (은닉 마르코프 모형을 이용한 회전체 결함신호의 패턴 인식)

  • Lee, Jong-Min;Kim, Seung-Jong;Hwang, Yo-Ha;Song, Chang-Seop
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.27 no.11
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    • pp.1864-1872
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    • 2003
  • Hidden Markov Model(HMM) has been widely used in speech recognition, however, its use in machine condition monitoring has been very limited despite its good potential. In this paper, HMM is used to recognize rotor fault pattern. First, we set up rotor kit under unbalance and oil whirl conditions. Time signals of two failure conditions were sampled and translated to auto power spectrums. Using filter bank, feature vectors were calculated from these auto power spectrums. Next, continuous HMM and discrete HMM were trained with scaled forward/backward variables and diagonal covariance matrix. Finally, each HMM was applied to all sampled data to prove fault recognition ability. It was found that HMM has good recognition ability despite of small number of training data set in rotor fault pattern recognition.

Detection of Oscillatory Pattern Signals and its Application to the Fault Diagnosis of a Boiler Drum-Level Control System (Oscillatory 파형감지에 의한 보일러 플랜트 드럼수위 제어계통의 고장진단)

  • Kim, Jae-Hwa;Seo, Yeol-Kyu;Jang, Tae-Gyu
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.1
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    • pp.44-51
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    • 1999
  • This paper proposes a new approach of plant fault diagnosis which is based on detecting the characteristic pattern signals and associating them with the corresponding faults. The new approach does not require analytic modeling of the target system but best reflects the expertise embedded in the experienced human operation by mimicking them in a systematic way. This paper intends to illustrate the feasibility of the proposed by developing the algorithms to detect and estimate the typical characteristic pattern signals, I. e., oscillatory patterns, and applying them to the diagnosis of various faults of a 500MW boiler control system including tube rupture, feed-water leak, and controller failure.

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A cohesive model for concrete mesostructure considering friction effect between cracks

  • Huang, Yi-qun;Hu, Shao-wei
    • Computers and Concrete
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    • v.24 no.1
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    • pp.51-61
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    • 2019
  • Compressive ability is one of the most important mechanical properties of concrete material. The compressive failure process of concrete is pretty complex with internal tension, shear damage and friction between cracks. To simulate the complex fracture process of concrete at meso level, methodology for meso-structural analysis of concrete specimens is developed; the zero thickness cohesive elements are pre-inserted to simulate the crack initiation and propagation; the constitutive applied in cohesive element is established to describe the mechanism of crack separation, closure and friction behavior between the fracture surfaces. A series of simulations were carried out based on the model proposed in this paper. The results reproduced the main fracture and mechanical feature of concrete under compression condition. The effect of key material parameters, structure size, and aggregate content on the concrete fracture pattern and loading carrying capacities was investigated. It is found that the inner friction coefficient has a significant influence on the compression character of concrete, the compression strength raises linearly with the increase of the inner friction coefficient, and the fracture pattern is sensitive to the mesostructure of concrete.

Postoperative radiotherapy appeared to improve the disease free survival rate of patients with extrahepatic bile duct cancer at high risk of loco-regional recurrence

  • Kim, Mi Young;Kim, Jin Hee;Kim, Yonghoon;Byun, Sang Jun
    • Radiation Oncology Journal
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    • v.34 no.4
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    • pp.297-304
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    • 2016
  • Purpose: To investigate the outcomes of postoperative radiotherapy (RT), in patients with extrahepatic bile duct (EHBD) cancer by comparing the survival rate between patients undergoing surgery alone or surgery plus postoperative RT, and to identify the prognostic factors affecting survival. Materials and Methods: Between 2000 and 2013, 52 patients with EHBD cancer underwent surgical resection. Of these, 33 patients did not receive postoperative RT (group I), and 19 patients did (group II). R1 resection was significantly more frequent in group II. The median radiation dose was 5,040 cGy. Results: The 3-year overall survival (OS) rate for group I and group II was 38% and 56%, respectively (p = 0.274). The 3-year disease free survival (DFS) rate for group I and group II was 20% and 31%, respectively (p = 0.049), and the 3-year loco-regional recurrence free survival (LRFS) rates were 19% and 58%, respectively (p = 0.002). Multivariate analyses showed that postoperative RT and lymphovascular invasion were independent prognostic factors for DFS and LRFS. Overall, 42 patients (80%) experienced treatment failure. Distant metastasis was the predominant pattern of failure in group II. Conclusion: Postoperative RT after surgical resection appeared to improve the loco-regional control and DFS rate. More effort is needed to reduce distant metastasis, the major pattern of failure, in patients who receive postoperative RT.

A Study on the Machine Learning Model for Product Faulty Prediction in Internet of Things Environment (사물인터넷 환경에서 제품 불량 예측을 위한 기계 학습 모델에 관한 연구)

  • Ku, Jin-Hee
    • Journal of Convergence for Information Technology
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    • v.7 no.1
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    • pp.55-60
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    • 2017
  • In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

Axial behavior of CFRP wrapped RC columns of different shapes with constant slenderness ratio

  • Narule, Giridhar N.;Bambole, Abhay N.
    • Structural Engineering and Mechanics
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    • v.65 no.6
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    • pp.679-687
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    • 2018
  • In composite materials technology, the fiber-reinforced polymers (FRP) have opened up new horizons in infrastructural engineering field for strengthening existing structures and components of structure. The Carbon fiber reinforced polymer (CFRP) sheets are well suited for RC columns to this application because of their high strength to weight ratio, good fatigue properties and excellent resistance to corrosion. The main focus of present experimental work is to investigate effect of shapes on axial behavior of CFRP wrapped RC columns having same cross-sectional area and slenderness ratio. The CFRP volumetric ratio and percentage of steel are also adopted constant for all the test specimens. A total of 18 RC columns with slenderness ratio four were cast. Nine columns were control and the rest of nine columns were strengthened with one layer of CFRP wrap having 35 mm of corner radius. Columns confined with CFRP wrap were designed using IS: 456:2000 and ACI 440.2R.08 provisions. All the test specimens were loaded for axial compression up to failure and failure pattern for each shaped column was investigated. All the experimental results were compared with analytical values calculated as per the ACI-440.2R-08 code. The test results clearly demonstrated that the axial behavior of CFRP confined RC columns is affected with the change in shapes. The axial deformation is higher in CFRP wrapped RC circular column as compared to square and rectangular columns. Stress-strain behaviour revealed that the yield strength gained from CFRP confinement was significant for circular columns as compare to square and rectangular columns. This behaviour may be credited due to effect of shape on lateral deformation in case of CFRP wrapped circular columns at effective confinement action.

Study of cracks in compressed concrete specimens with a notch and two neighboring holes

  • Vahab, Sarfarazi;Kaveh, Asgari;Shirin, Jahanmiri;Mohammad Fatehi, Marji;Alireza Mohammadi, Khachakini
    • Advances in concrete construction
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    • v.14 no.5
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    • pp.317-330
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    • 2022
  • This paper investigated computationally and experimentally the interaction here between a notch as well as a micropore under uniaxial compression. Brazilian tensile strength, uniaxial tensile strength, as well as biaxial tensile strength are used to calibrate PFC2d at first. Then, uniaxial compression test was conducted which they included internal notch and micro pore. Experimental and numerical building of 9 models including notch and micro pore were conducted. Model dimensions of models are 10 cm × 10 cm × 5 cm. Joint length was 2 cm. Joints angles were 30°, 45° and 60°. The position of micro pore for all joint angles was 2cm upper than top of the joint, 2 cm upper than middle of joint and 2 cm upper than the joint lower tip, discreetly. The numerical model's dimensions were 5.4 cm × 10.8 cm. The fractures were 2 cm in length and had angularities of 30, 45, and 60 degrees. The pore had a diameter of 1 cm and was located at the top of the notch, 2 cm above the top, 2 cm above the middle, and 2 cm above the bottom tip of the joint. The uniaxial compression strength of the model material was 10 MPa. The local damping ratio was 0.7. At 0.016 mm per second, it loaded. The results show that failure pattern affects uniaxial compressive strength whereas notch orientation and pore condition impact failure pattern. From the notch tips, a two-wing fracture spreads almost parallel to the usual load until it unites with the sample edge. Additionally, two wing fractures start at the hole. Both of these cracks join the sample edge and one of them joins the notch. The number of wing cracks increased as the joint angle rose. There aren't many AE effects in the early phases of loading, but they quickly build up until the applied stress reaches its maximum. Each stress decrease was also followed by several AE effects. By raising the joint angularities from 30° to 60°, uniaxial strength was reduced. The failure strengths in both the numerical simulation and the actual test are quite similar.

Whole-working history analysis of seismic performance state of rocking wall moment frame structures based on plastic hinge evolution

  • Xing Su;Shi Yan;Tao Wang;Yuefeng Gao
    • Earthquakes and Structures
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    • v.26 no.3
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    • pp.175-189
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    • 2024
  • Aiming at studying the plastic hinge (PH) evolution regularities and failure mode of rocking wall moment frame (RWMF) structure in earthquakes, the whole-working history analysis of seismic performance state of RWMF structure based on co-operation performance and PH evolution was carried out. Building upon the theoretical analysis of the elastic internal forces and deformations of RWMF structures, nonlinear finite element analysis (FEA) methods were employed to perform both Pushover analysis and seismic response time history analysis under different seismic coefficients (δ). The relationships among PH occurrence ratios (Rph), inter-story drifts and δ were established. Based on the plotted curve of the seismic performance states, evaluation limits for the Rph and inter-story drifts were provided for different performance states of RWMF structures. The results indicate that the Rph of RWMF structures exhibits a nonlinear evolution trend of "fast at first, then slow" with the increasing of δ. The general pattern is characterized by the initial development of beam hinges in the middle stories, followed by the development towards the top and bottom stories until the beam hinges are fully formed. Subsequently, the development of column hinges shifts from the bottom and top stories towards the middle stories of the structure, ultimately leading to the loss of seismic lateral capacity with a failure mode of partial beam yield, demonstrating a global yielding pattern. Moreover, the limits for the Rph and inter-story drifts effectively evaluate the five different performance states of RWMF structures.