• 한국생산제조학회지
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• 제22권1호
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• pp.22-28
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• 2013

Robotic GMA (Gas Metal Arc) welding process is one of widely acceptable metal joining process. The heat and mass inputs are coupled and transferred by the weld arc to the molten weld pool and by the molten metal that is being transferred to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired quality of the weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. MD (Mahalanobis Distance) technique was employed for investigating and modeling the GMA welding process and significance test techniques were applied for the interpretation of the experimental data. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats. First, a set of weldments without any faults were generated in a number of repeated sessions in order to be used as references. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. Statistical models developed from experimental results which can be used to quantify the welding quality with respect to process parameters in order to achieve the desired bead geometry based on weld quality criteria.

• Earthquakes and Structures
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• 제20권1호
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• pp.109-122
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• 2021

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

• 대한안전경영과학회지
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• 제24권4호
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• pp.1-8
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• 2022

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users' safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.243-250
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• 2005

Test methodology is diversity by devices and the number of test pattern is tremendous because the digital circuit includes TTL and CMOS family ICs as well as high density devices such as ROM and RAM. Accordingly, the quick and effective test strategy is required to enhance the test productivity. This paper proposes the test strategy which is able to be applied efficiently to the diversity devices on the digital circuit board by analyzing the structure and characteristic of the digital device. Especially, this test strategy detects the faulted digital device or the faulted digital circuit on the digital board using SSA(Serial Signature Analysis) technique based on the polynomial division theory The SSA technique identifies the faults by comparing the reminder from good device with reminder from the tested device. At this time, the reminder is obtained by enforcing the data stream obtained from output pins of the tested device on the LFSR(Linear Feedback Shift Register) representing the characteristic equation. Also, the method to obtain the optimal signature analysis circuit is explained by furnishing the short bit input streams to the long bit input streams to the LFSR having 8, 12, 16, 20bit input/output pins and by analyzing the occurring probability of error which is impossible to detect. Finally, the effectiveness of the proposed test strategy is verified by simulating the stuck at 1 errors or stuck at 0 errors for several devices on typical 8051 digital board.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권11호
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• pp.768-775
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• 2004

The SSA technique in the digital circuit test is required to be repeated the input pattern stream to n bits output nodes n times in case of using a multiplexor. Because the method adopting a parallel/serial bit convertor to remove this inefficiency has disadvantage of requiring the test time n times for a pattern, the test strategy is required, which can enhance the test productivity by reducing the test time based on simplified fault detection mechanism. Accordingly, this paper proposes a test strategy which enhances the test productivity and efficiency by appling PAS (Parallel Signature Analysis) technique to those after analyzing the structure and characteristics of the digital devices including TTL and CMOS family ICs as well as ROM and RAM. The PSA technique identifies the faults by comparing the reminder from good device with reminder from the tested device. At this time, the reminder is obtained by enforcing the data stream obtained from output pins of the tested device on the LFSR(Linear Feedback Shift Resister) representing the characteristic equation. Also, the method to obtain the optimal signature analyzer is explained by furnishing the short bit input streams to the long bit input streams to the LFSR having 8, 12, 16, 20bit input/output pins and by analyzing the occurring probability of error which is impossible to detect. Finally, the effectiveness of the proposed test strategy is verified by simulating the stuck at 1 errors or stuck at 0 errors for several devices on typical 8051 digital board.

• Transactions on Electrical and Electronic Materials
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• 제10권2호
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• pp.53-57
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• 2009

In this paper we develop a method to observe faults in semiconductor devices and transmission lines by calculating the variation of the reflection function in a dielectric microstrip line that has an open-ended termination containing an optically induced plasma region. It is analyzed with the assumption that the plasma is distributed homogeneously in laser illumination. With the non linear material of degradation, the concentration of the carrier in the part of the material has changed. Since the input wave has produced the phenomenon of reflection, the input signal to the open-ended microstrip lines can be observed on reflection to identify the location of the fault. The characteristic impedances resulting from the presence of plasma are evaluated by the transmission line model. The variation of the reflection wave in the microwave system has been calculated by using an equivalent circuit model. The transient response has been also evaluated theoretically for changing the phase of the variation in the reflection. The variation of characteristic response in differentially localized has been also evaluated analytically.

• Structural Monitoring and Maintenance
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• 제7권2호
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• 조명전기설비학회논문지
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• 제18권4호
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• pp.104-111
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• 2004

본 논문에서는 산업전반에 걸쳐 널리 사용되는 유도전동기의 고장상태를 검출하기 위해 선형판별분석기법에 기반을 둔 진단 알고리즘을 제안하고자 한다. 제안된 기법은 우선 주기별로 실험에 의해 측정된 전류값의 입력차원을 주성분분석기법을 이용하여 축소한 후 선형판별분석기법을 이용하여 고장상태별로 특징벡터를 추출한다. 다음으로 진단단계는 확보된 고장 종류별 특징벡터와 운전 시 입력되는 특징벡터간의 유클리디안 거리를 이용하여 유도전동기의 운전상태를 진단하는 구조로 되어있다. 마지막으로 선형판별분석기법의 타당성을 보이기 위해 노이즈가 있는 다양한 조건하에서 실험한 결과, 주성분분석기법만을 이용한 경우보다 우수한 결과를 나타냈다.

• 한국컴퓨터정보학회논문지
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• 제3권2호
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• pp.69-76
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• 1998

논리형 집적회로의 내부 결함을 검증하기 위해서는 설정된 초기 값을 전파하여 최종 출력 단에 나타난 값과 결함이 없을 경우의 출력 값을 비교함으로써 검증할 수 있다. 입력 단자의 수가 N인 회로에서 모든 내부신호 선의 결함을 검출하기 위해서는 최대 2N개의초기 입력 값들로 구성된 검증 패턴이 필요하다. 본 논문에서는 다 출력회로에서 2N개의 입력 패턴 중, 모든 신호선의 결함을 검출 할 수 있는 최소의 입력패턴 집합을 빠르고 정확하게 생성하기 위한 방법으로 다 출력회로를 출력과 연관된 세부회로로 분리하여 각각 검증함으로써 탐색공간을 줄이는 방법을 제안한다. 이는 입력 패턴의 길이가 상대적으로 줄어들 뿐 아니라 관련이 없는 신호 선을 탐색하지 않으므로 검증 패턴 생성 시간이 감소함으로써기존의 패턴 생성 알고리즘보다 효과적인 검증 패턴의 생성이 가능하다.

• 전력전자학회논문지
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• 제18권3호
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• pp.217-224
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• 2013

This paper presents a diagnostic method for a sparse matrix converter that detects faults in any single switch or a pair of switches. The sparse matrix converter is functionally equivalent to the standard matrix converter but has a reduced number of switches. The proposed diagnostic method is based in the measurement of input and output currents. The currents have respective characteristic according to the location of faulty switches. This method not only detects the switches of open-circuit fault but identifies the location of the faulty switching devices without complicated calculations. The simulation and experimental results verify that, based on the proposed method, the fault of sparse matrix converter can be easily and fast detected.