• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.1
• /
• pp.163-169
• /
• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• The Transactions of the Korea Information Processing Society
• /
• v.5 no.11
• /
• pp.2989-2996
• /
• 1998

As the circuit density of VLSI grows and its performance improves, delay fault testing of VLSI becomes very important. Delay faults in a circuit can be categorized into two classes, gate delay faults and path delay faults. This paper proposed two methods in dynamic memory usage in the path delay fault simulation. The first method is similar to that used in concurrent fault simulation for stuck-at faults and the second method reduces dynamic memory usage by not inserting a fault descriptor into the fault list when its value is X. The second method, called Implicit-X method, showed superior performance in both dynamic memory usage and simulation time than the first method, called Concurrent-Simulation-Like method.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.19 no.7
• /
• pp.726-735
• /
• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Journal of Advanced Navigation Technology
• /
• v.15 no.1
• /
• pp.18-24
• /
• 2011

This paper proposes fault diagnosis system which is able to diagnose the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. For this all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem by analyzing ship's operation data. To extract dynamic characteristics of these subsystems, log book data of container ship of H shipping company are used.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.2
• /
• pp.29-37
• /
• 1998

This paper describes the fault diagnosis based on real-time data of the inverter system for brush less DC motor drive. After identifying all the fault types in the inverter system, a preliminary typical analysis of fault types has been classified into the key fault symptoms. The predicted fault performances are then substantiated by using ACSL(Advanced Continuous Simulation Language), and the simulated results are composed of knowledge-base. The real-time measured data from the inverter system are compared with the simulated knowledge-base through the inference engine of expert system, which have been used to diagnose the fault causes. If some faults may occur in the inverter system, this system will be stopped. And then the expertise of elimination and remedial strategies about the fault causes, will be supplied rapidly to operator who doesn't know well about the inverter drive system.system.

• Proceedings of the KIEE Conference
• /
• 2009.07a
• /
• pp.315_317
• /
• 2009

전력계통은 발전, 송전, 변전, 배전계통으로 구성되지만 정전을 수반하는 대형고장은 전력계통 구성의 특성상 대부분 변전분야에서 발생하게 된다. 이러한 고장의 원인은 여러 가지로 분류할 수 있겠으나 특히 Human Error에 의한 고장 발생시 그 파급영향은 막대하다. 하지만 지금까지 변전설비의 고장 원인을 Human Error 측면에서 연구한 논문은 국내에서는 찾아보기가 힘들다. "전력계통의 고신뢰성 유지를 위한 변전분야 Human Error 예방대책"이란 논문이 2008년 한국전력 사내에서 발표된 정도이다. 본 논문에서는 해마다 끊이지 않고 반복되어지는 변전분야의 Human Error에 의한 고장에 대한 발생원인을 분석하고 현장 실행 위주의 예방대책을 제시하고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.219-220
• /
• 2019

교내 기자재가 고장이 나면 사용자는 수리하는 담당자에게 직접 연락을 하여 조취를 취한다. 고장수리 담당자의 처리 여부에 대한 확인은 유선으로 상호간에 확인하는 방법뿐이다. 본 연구를 통해 고장 난 기자재를 처리하고 싶은 사용자를 위해 실시간으로 처리 현황을 확인 할 수 있는 인터페이스와 기능을 지원하는 시스템을 개발하였다. 관리자는 해당 분류에 맞게 처리하는 담당자를 구별해 시스템에 글을 게시하고, 담당자는 담당 부서 게시물만 열람할 수 있도록 하여 빠른 일처리를 지원할 수있는 기능을 중점으로 개발하였다. 또한 하나의 기자재마다 책임자를 구별하고 처리 완료된 기자재의 종류의 기록과 통계를 사용자와 담당자가 보기 어려운 단위나 수치보다는 시각적인 효과를 받기 위한 그래프의 사용과 색상을 통하여 알림을 전해준다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.11B
• /
• pp.655-662
• /
• 2007

Monitoring systems used at present to operate HVAC(Heating, Ventilation and Air Conditioning) optimally do not have a function that enables to detect faults properly when there are faults of such as operating plants or performance falling, so they are unable to manage faults rapidly and operate optimally. In this paper, we have developed a classified rule-based fault detection system which can be inclusively used in HVAC system of a building by installation of sensor which is composed of HVAC system and required low costs compare to the model based fault detection system which can be used only in a special building or system. In order to experiment this algorithm, it was applied to HVAC system which is installed inside EC(Environment Chamber), verified its own practical effect, and confirmed its own applicability to the related field in the future.

• The KIPS Transactions:PartA
• /
• v.17A no.2
• /
• pp.81-92
• /
• 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%.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.110-110
• /
• 2022

본 연구는 자율운항 선박의 원격 고장 진단 기법 개발의 일부로 수행되었다. 특히, 엔진 연료 계통 장비로부터 계측된 시계열 데이터로부터 상태 진단을 위한 알고리즘 구현 결과를 제시하였다. 엔진 연료 펌프와 청정기를 가진 육상 실험 장비로부터 진동 시계열 데이터 계측하였으며, 이상 감지, 고장 분류 및 고장 예측이 가능한 심층 학습(Deep Learning) 및 기계 학습(Machine Learning) 알고리즘을 구현하였다. 육상 실험 장비에 고장 유형 별로 인위적인 고장을 발생시켜 특징적인 진동 신호를 계측하여, 인공 지능 학습에 이용하였다. 계측된 신호 데이터는 선행 발생한 사건의 신호가 후행 사건에 영향을 미치는 특성을 가지고 있으므로, 시계열에 내포된 고장 상태는 시간 간의 선후 종속성을 반영할 수 있는 학습 알고리즘을 제시하였다. 고장 사건의 시간 종속성을 반영할 수 있도록 순환(Recurrent) 계열의 RNN(Recurrent Neural Networks), LSTM(Long Short-Term Memory models)의 모델과 합성곱 연산 (Convolution Neural Network)을 기반으로 하는 Conv1D 모델을 적용하여 예측 정확성을 비교하였다. 특히, 합성곱 계열의 RNN LSTM 모델이 고차원의 순차적 자연어 언어 처리에 장점을 보이는 모델임을 착안하여, 신호의 시간 종속성을 학습에 반영할 수 있는 합성곱 계열의 Conv1 알고리즘을 고장 예측에 사용하였다. 또한 기계 학습 모델의 효율성을 감안하여 XGBoost를 추가로 적용하여 고장 예측을 시도하였다. 최종적으로 연료 펌프와 청정기의 진동 신호로부터 Conv1D 모델과 XGBoost 모델의 고장 예측 성능 결과를 비교하였다