• Title/Summary/Keyword: Defect probability

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Defect Detection algorithm of TFT-LCD Polarizing Film using the Probability Density Function based on Cluster Characteristic (TFT-LCD 영상에서 결함 군집도 특성 기반의 확률밀도함수를 이용한 결함 검출 알고리즘)

  • Gu, Eunhye;Park, Kil-Houm
    • Journal of Korea Multimedia Society
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    • v.19 no.3
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    • pp.633-641
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    • 2016
  • Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

Effect of Boundary Conditions of Failure Pressure Models on Reliability Estimation of Buried Pipelines

  • Lee, Ouk-Sub;Pyun, Jang-Sik;Kim, Dong-Hyeok
    • International Journal of Precision Engineering and Manufacturing
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    • v.4 no.6
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    • pp.12-19
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    • 2003
  • This paper presents the effect of boundary conditions in various failure pressure models published for the estimation of failure pressure. Furthermore, this approach is extended to the failure prediction with the aid of a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with each corrosion defect in buried pipelines for long exposure period with unit of years. A failure probability model based on the von-Mises failure criterion is adapted. The log-normal and standard normal probability functions for varying random variables are adapted. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically investigated for the corrosion pipeline by using an adapted failure probability model and varying failure pressure model.

Failure Probability of Corrosion Pipeline with Varying Boundary Condition

  • Lee, Ouk-Sub;Pyun, Jang-Sik
    • Journal of Mechanical Science and Technology
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    • v.16 no.7
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    • pp.889-895
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    • 2002
  • This paper presents the effect of external corrosion, material properties, operation condition and design thickness in pipeline on failure prediction using a failure probability model. The predicted failure assessment for the simulated corrosion defects discovered in corroded pipeline is compared with that determined by ANSI/ASME B31G code and a modified B31G method. The effects of environmental, operational, and random design variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress and pipe thickness on the failure probability are systematically studied using a failure probability model for the corrosion pipeline.

TFT-LCD Defect Blob Detection based on Sequential Defect Detection Method (순차적 결함 검출 방법에 기반한 TFT-LCD 결함 영역 검출)

  • Lee, Eunyoung;Park, Kil-Houm
    • Journal of the Korea Industrial Information Systems Research
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    • v.20 no.2
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    • pp.73-83
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    • 2015
  • This paper proposes a TFT-LCD defect blob detection algorithm using the sequential defect detection method. First, for every pixel, a defect possibility is determined by the intensity difference and the defect candidates are detected according to the sequential defect detection method. For detected candidate pixels, the defect probability that indicates a potential included in the defect according to the each step. By applying the morphological operation, blobs are comprised of the detected candidates and the defect blobs are detected using the defect possibility of blobs. The validity of the proposed method was demonstrated a simulated image and also then it was tested a real TFT-LCD image. By the experimental results, the proposed method is very effective in TFT-LCD detect detection.

A Study on the Estimating Burst Pressure Distributions for Reliability Assessment of API 5L X65 Pipes (API 5L X65 배관의 신뢰도 평가를 위한 파열압력 분포 추정에 관한 연구)

  • Kim, Seong-Jun;Kim, Dohyun;Kim, Cheolman;Kim, Woosik
    • Journal of the Korean Society for Quality Management
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    • v.48 no.4
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    • pp.597-608
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    • 2020
  • Purpose: The purpose of this paper is to present a probability distribution of the burst pressure of API 5L X65 pipes for the reliability assessment of corroded gas pipelines. Methods: Corrosion is a major cause of weakening the residual strength of the pipe. The mean residual strength on the corrosion defect can be obtained using the burst pressure code. However, in order to obtain the pipe reliability, a probability distribution of the burst pressure should be provided. This study is concerned with estimating the burst pressure distribution using Monte Carlo simulation. A response surface method is employed to represent the distribution parameter as a model of the corrosion defect size. Results: The experimental results suggest that the normal or Weibull distribution should be suitable as the probability distribution of the burst pressure. In particular, it was shown that the probability distribution parameters can be well predicted by using the depth and length of the corrosion defect. Conclusion: Given a corrosion defect on the pipe, its corresponding burst pressure distribution can be provided at instant. Subsequently, a reliability assessment of the pipe is conducted as well.

Effects of electronic energy deposition on pre-existing defects in 6H-SiC

  • Liao, Wenlong;He, Huan;Li, Yang;Liu, Wenbo;Zang, Hang;Wei, Jianan;He, Chaohui
    • Nuclear Engineering and Technology
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    • v.53 no.7
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    • pp.2357-2363
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    • 2021
  • Silicon carbide is widely used in radiation environments due to its excellent properties. However, when exposed to the strong radiation environment constantly, plenty of defects are generated, thus causing the material performance downgrades or failures. In this paper, the two-temperature model (2T-MD) is used to explore the defect recovery process by applying the electronic energy loss (Se) on the pre-damaged system. The effects of defect concentration and the applied electronic energy loss on the defect recovery process are investigated, respectively. The results demonstrate that almost no defect recovery takes place until the defect density in the damage region or the local defect density is large enough, and the probability of defect recovery increases with the defect concentration. Additionally, the results indicate that the defect recovery induced by swift heavy ions is mainly connected with the homogeneous recombination of the carbon defects, while the probability of heterogeneous recombination is mainly dependent on the silicon defects.

Estimation of Failure Probability Using Boundary Conditions of Failure Pressure Model for Buried Pipelines (파손압력모델의 경계조건을 이용한 매설배관의 파손확률 평가)

  • Lee, Ouk-Sub;Kim, Eui-Sang;Kim, Dong-Hyeok
    • Proceedings of the KSME Conference
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    • pp.310-315
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    • 2003
  • This paper presents the effect of boundary condition of failure pressure model for buried pipelines on failure prediction by using a failure probability model. The first order Taylor series expansion of the limit state function is used in order to estimate the probability of failure associated with various corrosion defects for long exposure periods in years. A failure pressure model based on a failure function composed of failure pressure and operation pressure is adopted for the assessment of pipeline failure. The effects of random variables such as defect depth, pipe diameter, defect length, fluid pressure, corrosion rate, material yield stress, material ultimate tensile strength and pipe thickness on the failure probability of the buried pipelines are systematically studied by using a failure probability model for the corrosion pipeline.

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Estimating Outbreak Probabilities of Systems and Components with Masked Data (마스크 데이터를 이용한 컴포넌트의 고장발생확률 추정)

  • 박창규
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.25 no.6
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    • pp.7-11
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    • 2002
  • This paper estimates defect and outbreak probabilities of each individual component from some subset of masked data where the exact component causing system failure might be unknown. A system consists of k components that fails whenever there is a defect in at least one of the components. Due to cost and time constraints it is not feasible to learn exactly which components are defective. Because, test procedures ascertain that the defective components belong to some subset of the k components. This phenomenon is termed masking. We describe a, b, c type in which a sample of masked subsets is subjected to intensive failure analysis. This recorded data of a, b, c type enables maximum likelihood estimation of defect probability of each individual component and leads to outbreak of the defective components in future masked failures.

Estimation of sewer deterioration by Weibull distribution function (와이블 분포함수를 이용한 하수관로 노후도 추정)

  • Kang, Byongjun;Yoo, Soonyu;Park, Kyoohong
    • Journal of Korean Society of Water and Wastewater
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    • v.34 no.4
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    • pp.251-258
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    • 2020
  • Sewer deterioration models are needed to forecast the remaining life expectancy of sewer networks by assessing their conditions. In this study, the serious defect (or condition state 3) occurrence probability, at which sewer rehabilitation program should be implemented, was evaluated using four probability distribution functions such as normal, lognormal, exponential, and Weibull distribution. A sample of 252 km of CCTV-inspected sewer pipe data in city Z was collected in the first place. Then the effective data (284 sewer sections of 8.15 km) with reliable information were extracted and classified into 3 groups considering the sub-catchment area, sewer material, and sewer pipe size. Anderson-Darling test was conducted to select the most fitted probability distribution of sewer defect occurrence as Weibull distribution. The shape parameters (β) and scale parameters (η) of Weibull distribution were estimated from the data set of 3 classified groups, including standard errors, 95% confidence intervals, and log-likelihood values. The plot of probability density function and cumulative distribution function were obtained using the estimated parameter values, which could be used to indicate the quantitative level of risk on occurrence of CS3. It was estimated that sewer data group 1, group 2, and group 3 has CS3 occurrence probability exceeding 50% at 13th-year, 11th-year, and 16th-year after the installation, respectively. For every data groups, the time exceeding the CS3 occurrence probability of 90% was also predicted to be 27th- to 30th-year after the installation.

Defect Detection and Defect Classification System for Ship Engine using Multi-Channel Vibration Sensor (다채널 진동 센서를 이용한 선박 엔진의 진동 감지 및 고장 분류 시스템)

  • Lee, Yang-Min;Lee, Kwang-Young;Bae, Seung-Hyun;Jang, Hwi;Lee, Jae-Kee
    • The KIPS Transactions:PartA
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    • v.17A no.2
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    • pp.81-92
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    • 2010
  • There has been some research in the equipment defect detection based on vibration information. Most research of them is based on vibration monitoring to determine the equipment defect or not. In this paper, we introduce more accurate system for engine defect detection based on vibration information and we focus on detection of engine defect for boat and system control. First, it uses the duplicated-checking method for vibration information to determine the engine defect or not. If there is a defect happened, we use the method using error part of vibration information basis with error range to determine which kind of error is happened. On the other hand, we use the engine trend analysis and standard of safety engine to implement the vibration information database. Our simulation results show that the probability of engine defect determination is 100% and the probability of engine defect classification and detection is 96%.