In this paper, each controller's unique ID and PICO oscilloscope were used to measure the voltage waveform of each CAN communication line, and compare and analyze the serial decoding results. Using the voltage change level of the CAN communication line, it was possible to check whether the CAN-High line and the Low line were disconnected. And it was possible to infer the circuit disconnection point between the controller and the controller only with the unique ID information of each controller. And when the CAN-High circuit was disconnected, the voltage of the high line was measured at the same voltage level as the Low line.

머신러닝 기법의 발달과 함께 기계에서 발생하는 다양한 종류(진동, 온도, 유량 등)의 데이터를 활용하여 기계의 상태를 진단하고 이상 탐지 및 비정상 분류 연구도 활발히 진행되고 있다. 특히 진동 데이터를 활용한 회전 기계의 상태 진단은 전통적인 기계 상태 모니터링 분야로 오랜 기간 동안 연구가 진행되었고, 연구 방법 또한 매우 다양하다. 본 연구에서는 가정용 에어컨에 사용되는 로터리 압축기에 가속도계를 직접 설치하여 진동 데이터를 수집하는 실험을 진행하였다. 데이터 부족 문제를 해결하기 위해 데이터 분할을 수행하였으며, 시간 영역에서의 진동 데이터로부터 통계적, 물리적 특징들을 추출한 후, Chi-square 검증을 통해 고장 분류 모델의 주요 특징을 추출하였다. SVM(Support Vector Machine) 모델은 압축기의 정상 혹은 이상 유무를 분류하기 위해 개발되었으며, 파라미터 최적화를 통해 분류 정확도를 개선하였다.

While inspecting the Natanz Nuclear Site (Iran) on October 6, 2019, an inspector of the IAEA was found to carry suspicious materials. She was prevented from entering the site by the Iranian authorities and was shortly returned to the Agency’s headquarters. Iran could not prosecute her, because of the inspector’s immunity by the Agency. However, in addition to exhaustion of the dispute settlement mechanisms predicted in the Agreement on the Privileges and Immunities of the IAEA which has also provided the recourse to the ICJ, Iran may prosecute the Agency by attributing the conduct of the inspector to the Agency in the national courts of non-member States of the Agency that has not accepted the immunity of international organizations as customary international law. This article deals with the immunity of international organizations and its agents, as well as examining the Agency inspector’s fault in inspecting the Natanz Nuclear Site and the Judicial Mechanisms for follow-ups.

The empirical Green’s function method is applied to the foreshock and the mainshock of the 2016 Gyeongju earthquake to simulate strong ground motions of the mainshock and scenario earthquake at seismic stations of seven metropolises in South Korea, respectively. To identify the applicability of the method in advance, the mainshock is simulated, assuming the foreshock as the empirical Green’s function. As a result of the simulation, the overall shape, the amplitude of PGA, and the duration and response spectra of the simulated seismic waveforms are similar with those of the observed seismic waveforms. Based on this result, a scenario earthquake on the causative fault of Gyeongju earthquake with a moment magnitude 6.5 is simulated, assuming that the mainshock serves as the empirical Green’s function. As a result, the amplitude of PGA and the duration of simulated seismic waveforms are significantly increased and extended, and the spectral amplitude of the low frequency band is relatively increased compared with that of the high frequency band. If the empirical Green’s function method is applied to several recent well-recorded moderate earthquakes, the simulated seismic waveforms can be used as not only input data for developing ground motion prediction equations, but also input data for creating the design response spectra of major facilities in South Korea.

Industrial Motors diagnostic equipment is highly dependent on the automation system, so if there are defects in the automation equipment, it can only rely on the operator’s intuitive judgment.To help with intuitive judgment, Park’s Vactor Approach(PVA) represents the current signal as a pattern of circles, so it can tell if a fault occurs when the circle is distorted. However, the failure to judge the degree of distortion of the circle pattern is the basis of the fault, so it will face difficulties. In this paper, in order to compare the faults of PVA, the period of d-axis current of PVA pulsation was mastered, so that two phase differences occurred in the same signal source. Through experiments, it is confirmed that this is a 90 degree cross formation of PVA, which is convenient for judging from the vision that there is no fault, thus helping the operator to make intuitive judgment.

2015년 12월 22일 전북 익산시 북쪽 지역에서 발생한 익산지진(ML=3.85)의 본진 및 2개 여진에 대해 FOCMEC (FOCal MEChanism determination) 프로그램을 이용하여 단층면해를 구하였다. 본진의 단층면해는 북동-남서 또는 북서-남동 방향의 단층면을 가지는 역단층성 주향이동단층운동의 특성을 보이며, 이는 남한 내륙지진의 단층운동 특성과 거의 유사하다. 익산지진 전후 발생한 미소지진 이벤트를 검출하고자 2015년 12월 15일~2016년 01월 22일 진앙으로부터 반경 100 km 이내에 위치한 13개 관측소에 기록된 연속 지진파형 자료를 PQLII 프로그램(PASSCAL, 2017)으로 분석하고, 19개 지진의 진원지를 새로 결정하였다. 미소지진의 진앙 분포는 특정 단층 혹은 구조선에 집중되는 현상은 나타나지 않았으나, 익산지진과 3개의 여진은 한곳에 집중 분포한다. 익산지진의 진도 분포는 전화 문의 및 피해 접수 자료를 수집하여 구하였으며, 또한, 각 관측소에 기록된 PGA (Peak Ground Acceleration)를 이용하여 계기 진도를 도출하였다. 그 결과 익산지진의 MMI 진도등급은 최대 V로 평가된다.

Ocean economy plays a crucial role in the strengthening maritime safety industry and in the welfare of human beings. Electric Submersible Pumps (ESP) have been widely used in floating platforms on the sea to provide oil for machines. However, the ESP fault may lead to ocean environment pollution, on the other hand, a timely fault diagnosis of ESP can improve the ocean economy. In order to meet the strict regulations of the ocean economy and environmental protection, the fault diagnosis of ESP system has become more and more popular in many countries. The vibration mechanical models of typical faults have been able to successfully diagnose the faults of ESP. And different types of sensors are used to monitor the vibration signal for the signal analysis and fault diagnosis in the ESP system. Meanwhile, physical sensors would increase the fault diagnosis challenge. Nowadays, the method of neural network for the fault diagnosis of ESP has been applied widely, which can diagnose the fault of an electric pump accurately based on the large database. To reduce the number of sensors and to avoid the large database, in this paper, algorithms are designed based on feature extraction to diagnose the fault of the ESP system. Simulation results show that the algorithms can achieve the prospective objectives superbly.

In this paper, classification of spall and crack faults of gear teeth is studied by applying the ensemble empirical mode decomposition(EEMD) for the gear transmission error(TE). Finite element models of the gears with the two faults are built, and TE is obtained by simulation of the gears under loaded contact. EEMD is applied to the residuals of the TE which are the difference between the normal and faulty signal. From the result, the difference of spall and crack faults are clearly identified by the intrinsic mode functions(IMF). A simple test bed is installed to illustrate the approach, which consists of motor, brake and a pair of spur gears. Two gears are employed to obtain the TE for the normal, spalled, and cracked gears, and the type of the faults are separated by the same EEMD application process. In order to quantify the results, crest factors are applied to each IMF. Characteristics of spall and crack are well represented by the crest factors of the first and the third IMF, which are used as the feature signals. The classification is carried out using the Bayes decision theory using the feature signals acquired through the experiments.

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today’s systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

Emission regulation by Europe and the U.S. on air pollutants from diesel automobiles which include particulate matter and nitrogen oxides are becoming stricter than ever. The SCR uses Urea, which is NH3 diluted by water, and chemically reacts with emission gases reducing over 90% of the NOx created. In this study several problems were observed in a SCR system and Engine control system. Through this analysis we can find out more effective repair factors from the various fault in SCR component.

본 연구에서는 와이어로프의 국부손상 검색을 위해 누설자속기법을 적용하였다. 와이어로프 구조물에 적용하기 위해 리프트오프의 발생을 최소화한 4채널 누설자속 센서헤드를 제작하였고, 이를 사용하여 와이어로프의 국부손상 검색실험을 수행하였다. 국부손상 검색실험을 위해 와이어로프를 준비하였고, 다양한 원주방향을 가지는 부분 단선 손상들을 발생시켰다. 제작된 자속누설 센서헤드를 이용하여 와이어로프 시편의 자속신호를 스캔하였고, 노이즈의 영향을 최소화하고 자속신호의 해상도를 향상시키고자 자속 신호를 미분하여 순간변화량을 손상 검색에 활용하였다. 객관적인 손상 판단을 위해 각 채널에서 계측된 자속신호를 GEV분포를 이용해 설정된 임계값과 비교하였다. 최종적으로 임계값을 초과한 부분의 길이방향 및 원주방향 위치를 실제 손상과 비교함으로써 본 기법의 국부손상 검색 가능성을 살펴보았다.

한반도 서부인 당진 지역에서 새로이 발견된 제4기 진관단층의 운동 특성과 단층활동 이력에 대한 윤곽을 잡기 위해 몇 개의 단층비지대로 구성된 진관단층의 기하 분석과 단층비지 물질에 대한 ESR 연령측정을 하였다. 진관단층은 정단층으로, N55˚E 주향의 이 단층은 낮은 곳에서는 경사가 57˚NW, 상부로 갈수록 경사가 수직에 가깝게 변화를 보여 주며 리스트릭 단층임을 보여준다. 단층비지대의 두께는 노두 하부의 기반암과 기반암 사이에서 2~3 cm, 중상부의 기반암과 제4기층 사이 경계에서는 20~30 cm 폭으로 보다 두껍다. 후자의 경우에 있어서 3개 이상의 단층비지대가 분리되거나 합쳐지기도 하며, 부분적으로는 서로 중첩되기도 하는데 이는 서로 다른 단층운동 사건들이 있었음을 지시한다. 지질단면도로부터 산출된 단층의 누적 총변위는 약 10 m이며 누적 총변위와 단층의 길이 사이 경험식에 따르면 약 2.5 km의 단층 길이에 해당된다고 할 수 있다. 그러므로 단층의 길이를 제대로 알기 위해 보다 연구가 요구된다. 단층대를 따라 채취한 세 단층암 샘플에 대한 ESR 연령측정 결과, 651±47, 649±96, 436±66ka 등으로 나타났으며 최소 두번의 단층운동이 있었음을 지시한다. 단층면에서 관찰되는 단층조선은 순전한 경사이동만을 지시하며(정단층 운동), 동북동-서남서 주향의 진관단층은 북북서-동남동 방향의 수평 최소 압축응력 환경에서 단층운동이 있었음을 제시한다.

This study was carried out to analyze water suspension in the water supply system through fault tree analysis. And quantitative factors was evaluated to minimize water suspension. Consequently the aim of this study is to build optimal planning by analyzing scenarios for water suspension.Accordingly the fault tree model makes it possible to estimate risks for water suspension, current risks is 92.23 m3/day. The result of scenario analysis by pipe replacement, risks for water suspension was reduced 7.02 m3/day when replacing WD4 pipe. As a result of scenario analysis by water district connections, the amount of risk reduction is maximized when it is connecting to network pipe of D Zone. Therefore, connecting to network pipe for D Zone would be optimal to reduce risk for water suspension.

Fault plane solutions in North Korea and the northern part of the Yellow Sea (37.5˚N-40.5˚N, 124.5˚E-128.5˚E) was studied for the earthquakes that occurred from November, 2008 to May, 2013. The analysis was based on the data collected from seismic networks in Korea and China. Fault plane solutions were obtained from P and SH wave polarities and SH/P amplitude ratioes. Most earthquakes exhibited predominantly strike-slip fault characteristics with NNE-SSW or WNW-ESE nodal planes. The P-axes trends are mainly NE-SW or ENE-WSW direction in the northern part of the Yellow Sea and inland area of North Korea except some areas in the Hwanghae province. Fault plane solutions and main axis of stress field in the study region were similar to those observed in the southern part of the Korean Peninsula.

Multivariate control charts are widely needed to monitor the production processes in various industry. Among the several multivariate control charts, control chart have been used of the typical technique. The control chart shows a statistic that represents observed variables and monitors the process through the statistic. In this case, the statistic generally have the limit that any variables affect to that statistic. To solve this problem, some studies have been progressed in the meantime. The representative method is to disassemble total statistic into each of the variable value and make a decision the parameters with large values than threshold value as a main cause. However, the means is requested to follow the normal distribution. To settle this problem, the bootstrap technique that don't be needed the probability distribution was introduced in 2011. In this paper, I introduced the detection technique of the fault variables using multiple regression analysis. There are two advantages; First, it is possible to use less samples than the ascertainment technique applying to bootstrap. Second, the technique using the regression analysis is easy to apply to the actual environment because the global threshold value is used.