• 한국컴퓨터정보학회논문지
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• 제16권3호
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• pp.141-147
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• 2011

본 논문은 유도전동기의 고장검출 및 진단을 위한 효율적인 2-단계 고장예측 알고리즘을 제안한다. 첫 번째 단계에서는 고장 패턴 추출을 위해 선형 예측 부호화 (Linear Predictive Coding: LPC) 기법을 사용하고, 두 번째 단계에서는 고장 패턴 매칭을 위해 동적시간교정법 (Dynamic Time Warping: DTW)을 사용한다. 유도전동기에서 정상 및 각종 이상 상태의 조건을 발생시켜 추출한 샘플링 주파수 8kHz, 샘플링 시간 2.2초의 정상상태 및 비정상 상태의 진동데이터 8개를 사용하여 모의 실험한 결과, 제안한 고장예측 알고리즘은 기존의 고장진단 알고리즘보다 약 45%의 정확도 향상을 보였다. 또한 TI사의 TMS320F2812 DSP를 내장한 테스트베드 시스템을 제작하여 제안한 고장예측 알고리즘을 구현하고 검증하였다.

• Journal of Advanced Marine Engineering and Technology
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• 제30권2호
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• pp.253-259
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• 2006

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 전기학술대회논문집
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• pp.251-256
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• 2005

There is few study which automatically diagnose the fault from ship's monitored signal. The bigger control and monitoring system is, the more important fault diagnosis and maintenance is to reduce damage brought forth by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault and is composed to fault detection knowledge base and fault diagnosis knowledge base. 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 verifying capability of fault detection, diagnosis and prediction, Fault Management System(FMS) is developed by C++. Simulation experiment by FMS is carried out with population data set made by log book data of 2 months duration from a large full container ship of H shipping company.

• 한국산업융합학회 논문집
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• 제23권5호
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• 한국멀티미디어학회논문지
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• 제20권11호
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• pp.1811-1819
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• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.

• 한국컴퓨터정보학회논문지
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• 제28권10호
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• pp.1-8
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• 2023

자동차의 주요 부품인 휠 베어링에 결함이 생기면 교통사고등 문제를 발생시켜 이를 해결하기 위해 빅데이터를 수집해서 예측진단 및 관리 기술을 통한 휠 베어링의 고장 유무 및 고장 유형을 조기에 알려 주는 알고리즘과 모니터링 시스템 개발이 필요하다. 본 논문에서는 이러한 지능형 휠 허브 베어링 정비 시스템 구현을 위해 신뢰성 및 건전성에 대한 모니터링용 센서 및 예측 진단하는 알고리즘이 탑재된 임베디드 시스템을 개발하였다. 사용된 알고리즘은 휠 베어링에 설치된 가속도 센서로부터 진동 신호를 취득하고 이를 신호 처리기법, 결함주파수 분석, 건전성 특징 인자정의 등의 과정을 빅데이터 기술을 통해 고장을 예측하고 진단할 수 있다. 구현된 알고리즘은 진동 주파수 성분들은 최소화하고 휠 베어링에서 발생하는 진동 성분을 극대화할 수 있는 안정 신호 추출 알고리즘을 적용하고, 필터를 활용한 노이즈 제거에서는 인공지능 기반의 건전성 추출 알고리즘을 적용하였으며, FFT를 통한 결함 주파수를 분석하여 고장 특성인자 추출을 통한 고장을 진단하였다. 본 시스템의 성능 목표는 12,800ODR 이상으로 시험 결과를 통해 목표치를 만족하였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• 한국태양에너지학회 논문집
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• 제39권6호
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• pp.55-65
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• 2019

With the rapid development of the national industry, the importance of electrical safety was recognized because of a lot of new electrical equipment are installing and the electrical accidents have been occurring annually. Today, the electrical equipments is inspect by using the portable Infrared thermal imaging camera. but the most negative element of using the camera is inspected for only state of heating, the reliable diagnosis is depended with inspector's knowledge, and real-time monitoring is impossible. This paper present the infrared thermal imaging safety diagnosis system. This system is able to monitor in real time, predict the state of fault, and diagnose the state with analysis of thermal and power data. The system consists of a main processor, an infrared camera module, the power data acquisition board, and a server. The diagnostic algorithm is based on a mathematical model designed by analyzing the Pearson's Correlation Coefficient between temperature and power data. To test the prediction algorithm, the simulations were performed by damaging the terminals or cables on the switchboard to generate a large amount of heat. Utilizing these simulations, the developed prediction algorithm was verified.

• 한국항해항만학회지
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• 제46권3호
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• pp.280-288
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• 2022

본 연구는 정상 가동 중에도 회전수가 변하는 기기의 이상 및 고장 진단 방안을 다루고 있다. 회전수가 변함에 따라 비정상적 시계열 특성을 내포한 센서 데이터에 기계학습을 적용할 수 있는 절차를 제시하고자 하였다. 기계학습으로는 k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Random Forest을 사용하여 이상 및 고장 진단을 수행하였다. 또한 진단 정확성을 비교할 목적으로 이상 감지에 오토인코더, 고장진단에는 합성곱 기반의 Conv1D도 추가로 이용하였다. 비정상적 시계열로부터 통계 및 주파수 속성으로 구성된 시계열 특징 벡터를 추출하고, 추출된 특징 벡터에 정규화 및 차원 축소 기법을 적용하였다. 특징 벡터의 선택과 정규화, 차원 축소 여부에 따라 달라지는 기계학습의 진단 정확도를 비교하였다. 또한, 적용된 학습 알고리즘 별로 초매개변수 최적화 과정과 적층 구조를 설명하였다. 최종적으로 기존의 심층학습과 비교하여, 기계학습도 가변 회전기기의 고장을 정확하게 진단할 수 있는 절차를 제시하였다.

• Design & Manufacturing
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• 제16권4호
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.