• Title/Summary/Keyword: 고장진단엔진

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Outlier Detection and Labeling of Ship Main Engine using LSTM-AutoEncoder (LSTM-AutoEncoder를 활용한 선박 메인엔진의 이상 탐지 및 라벨링)

  • Dohee Kim;Yeongjae Han;Hyemee Kim;Seong-Phil Kang;Ki-Hun Kim;Hyerim Bae
    • The Journal of Bigdata
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    • v.7 no.1
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    • pp.125-137
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    • 2022
  • The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

Development of Korean Condition Based Maintenance Systems to Monitor Naval Weapon Systems (해군 무기체계 한국형 상태진단시스템 발전방향 연구)

  • Oh, Kyungwon
    • Journal of Aerospace System Engineering
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    • v.10 no.4
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    • pp.67-74
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    • 2016
  • The primary aim for using a Korean Condition Based Maintenance (CBM) system is to maintain military operational readiness using Interactive Collection Analysis Systems (ICAS) installed on naval vessels. Other aims are to preemptively provision maintenance and supply functions, to guarantee economical management of logistical assets, and to implement data driven equipment life cycle management. In order to accomplish these aims, it is necessary to establish standard system conditions. However, because manufacturers do not provide the technology necessary for maintenance management, it is required to retain component performance maps for each piece of equipment, and to accumulate data about frequently occurring fault patterns. This study confirms the validity of component performance maps using micro gas turbines and provides accumulated data on machine break downs. This would allow real time equipment performance checks and present performance trends. Then analysis would provide solutions for maintaining the best machine operating conditions with detailed maintenance manuals for operators. This study is a basis for further research to investigate additional ways to develop CBM using data obtained from naval vessels used in actual military operations.

The Misfire Detection and Intensity Interpretation using Breakdown Voltage Characteristics (브레이크다운전압 특성을 이용한 엔진실화의 검출 및 강도해석)

  • 고용수;박재근;조민석;황재원;채재우
    • Transactions of the Korean Society of Automotive Engineers
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    • v.7 no.6
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    • pp.42-48
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    • 1999
  • Engine misfire causes of the negative effect on exhaust emission such as HC, CO, and NOX . Moreover, it causes damage to the three-way-catalyst(TWC) system permanently. The crankshaft velocity fluctuation(CVF) method has been applied for the real cars as misfire detection system usually, which utilizes the crank angle sensor input to calculate the variation of the crankshaft rotational speed. But this approach has the limit due to the fact that three could be problem under certain engine condition like as deceleration or high speed condition . Therefore the development of new methods are requested today. This study introduced the new method of misfire detection using breakdown voltage(BDV) characteristics between spark plug electrouds.

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Case Study of Intermittent Engine Hesitation Fault Diagnosis By CKPS Fault (LPI차량에서 CKPS불량으로 주행 중 간헐적인 엔진부조 현상의 고장진단)

  • Kim, Sung Mo
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.23 no.6
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    • pp.624-629
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    • 2014
  • The purpose of this study is to carry out the task of engine hesitation which occurred intermittently in driving due to the defective CKPS of LPI vehicles. As the result of the wrong data from the equipment of D-logger, the signal error of CKPS caused the engine hesitation. We performed a study in the followings to analyze and investigate the cause effectively. First, we have investigated the control wiring harness and connector pin contact defect inspection. Second, we have inspected the defection of CKPS separately. From this study, it was found that the engine hesitation were caused by the bad durability and we have showed how to diagnosis the fault of the engine hesitation intermittently while driving. Therefore, it is determined that we have to improve the durability of the CKPS through a strict quality control and to increase the reliability.

Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Model (HMM기반 소음분석에 의한 엔진고장 진단기법)

  • Le, Tran Su;Lee, Jong-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39A no.5
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    • pp.244-250
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    • 2014
  • The Most Serious Engine Faults Are Those That Occur Within The Engine. Traditional Engine Fault Diagnosis Is Highly Dependent On The Engineer'S Technical Skills And Has A High Failure Rate. Neural Networks And Support Vector Machine Were Proposed For Use In A Diagnosis Model. In This Paper, Noisy Sound From Faulty Engines Was Represented By The Mel Frequency Cepstrum Coefficients, Zero Crossing Rate, Mean Square And Fundamental Frequency Features, Are Used In The Hidden Markov Model For Diagnosis. Our Experimental Results Indicate That The Proposed Method Performs The Diagnosis With A High Accuracy Rate Of About 98% For All Eight Fault Types.

Research on Vehicle Diagnostic and Monitoring technology Using WiBro Portable Device (와이브로 휴대기기를 사용한 차량진단 및 모니터링 기술에 관한 연구)

  • Ryoo, Hee-Soo;Won, Yong-Gwan;Park, Kwon-Chul;Ahn, Yong-Beom
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.47 no.10
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    • pp.17-26
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    • 2010
  • This is concerned with the technology to monitor the vehicle operation, failure and disorder by using WiBro portable device. More precisely, the technology makes it possible that the information collection device is connected to both ECU(Electronic Control Unit) which is the device for controlling engine, transmission, brake, air-bag, etc that are connected to in-vehicle network and OBD-II connector that is for data collection from various sensors. In addition, with a WiBro portable device (cell phone, PDA, PMP, UMPC, etc). equipped with a vehicle diagnostic programs, information for operation, failure and malfunction can be obtained and analyzed in real-time, and alarm is alerted when the vehicle is in abnormal status, which makes the early reactions to the status. Furthermore, the collected data can be sent through WiBro network to the server managed by the company specialized in managing the vehicles, thus the technology could help the drivers who have less knowledge about their auto-vehicles have safe and economic driving. There is always a possibility of malfunction due to various types of noise that are caused by wring-harness when the device is wired-connected. In this research, in order to overcome this problem, we propose a system configuration that can do monitoring and diagnosis with a device for collecting data from vehicle and a personal WiBro device. Also, we performed research on data acquisition and interlock for the system defined by the definition for information and data sharing platform.

A Study on the Development of Capacitor Exchange Type GDU of Propulsion Control Device of Electric Railway Vehicle Capable of Life Diagnosis (수명진단이 가능한 전기철도차량 추진제어장치의 커패시터 교환 형 GDU 개발에 관한 연구)

  • Kim, Sung Joon;Chae, Eun Kyung;Kang, Jeong Won
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.8 no.7
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    • pp.475-484
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    • 2018
  • The propulsion control device of an electric railway vehicle is a key main component corresponding to an engine of an automobile, and a device for controlling this is a device called a GDU (Gate Drive Unit). Also, when the frequency of failure of the propulsion control system was analyzed, the nonconformity ratio of GDU was the highest. GDU was not able to access core technologies due to the introduction of foreign products, and there were general problems with overall maintenance activities due to discontinuation of GDU of the manufacturer. The GDU has reached the end of its life with 23 to 14 years of long-term use.In order to solve these problems, this study was designed to identify the proper life span by analyzing compatible GDU's acquisition and failure, and to improve the existing system of maintenance focusing on health inspection. Maintenance of the components with a short life span compared to the entire service life is essential. Most foreign parts introduced at the beginning of the construction are not replaced due to technical problems or long-term operation. However, due to the characteristics of railway vehicles with a long life span of more than 25 years, it is necessary to maintain them for a long period of time. The study should be more concrete and empirical. The replacement type GDU of capacitors was able to easily measure the life of the capacitance by removing the capacitor modules, measure the life span of each unit test, and accurately perform preventive maintenance of the capacitor.

A Signal Processing Technique for Predictive Fault Detection based on Vibration Data (진동 데이터 기반 설비고장예지를 위한 신호처리기법)

  • Song, Ye Won;Lee, Hong Seong;Park, Hoonseok;Kim, Young Jin;Jung, Jae-Yoon
    • The Journal of Society for e-Business Studies
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    • v.23 no.2
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    • pp.111-121
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    • 2018
  • Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.