• Title, Summary, Keyword: Fault diagnosis

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Integrated Fault Diagnosis Algorithm for Driving Motor of In-wheel Independent Drive Electric Vehicle (인휠 독립 구동 전기 자동차의 구동 모터 통합 고장 진단 알고리즘)

  • Jeon, Namju;Lee, Hyeongcheol
    • Transactions of the Korean Society of Automotive Engineers
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    • v.24 no.1
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    • pp.99-111
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    • 2016
  • This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through $Carsim^{(R)}$ and MATLAB/$Simulink^{(R)}$ cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/$Simulink^{(R)}$ interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

Development of Multiple Fault Diagnosis Methods for Intelligence Maintenance System (지적보전시스템의 실시간 다중고장진단 기법 개발)

  • Bae, Yong-Hwan
    • Journal of the Korean Society of Safety
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    • v.19 no.1
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    • pp.23-30
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    • 2004
  • Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

The Design and Implementation of a Fault Diagnosis on an Electronic Throttle Control System (전자식 스로틀 제어시스템을 위한 오류 자기진단 기능 설계 및 구현)

  • Kang, Jong-Jin;Lee, Woo-Taik
    • Transactions of the Korean Society of Automotive Engineers
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    • v.15 no.6
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    • pp.9-16
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    • 2007
  • This paper describes the design and implementation of the fault diagnosis on the Electronic Throttle Control(ETC) System. The proposed fault diagnosis consists of an input signal, actuator and a processor diagnosis. The input signal diagnosis can detect the faults of the ETC system's input signals such as the position sensor fault, source voltage fault, load current fault, and desired position fault. The actuator diagnosis is able to detect the actuator fault due to the actuator aging and an obstacle which interfere in the movement of the actuator. The processor diagnosis detects the fault which prevents the microprocessor from operating the ETC software. In order to protect the breakdown of the ETC system and assure the driving safety, appropriate reactions are also proposed according to the detected faults. The safety and reliability of the ETC system can be improved by the proposed fault diagnosis.

Agent based real-time fault diagnosis simulation (에이젼트기반 실시간 고장진단 시뮬레이션기법)

  • 배용환;이석희;배태용;이형국
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.670-675
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    • 1994
  • Yhis paper describes a fault diagnosis simulation of the Real-Time Multiple Fault Dignosis System (RTMFDS) for forcasting faults in a system and deciding current machine state from signal information. Comparing with other diagnosis system for single fault,the system developed deals with multiple fault diagnosis,comprising two main parts. One is a remotesignal generating and transimission terminal and the other is a host system for fault diagnosis. Signal generator generate the random fault signal and the image information, and send this information to host. Host consists of various modules and agents such as Signal Processing Module(SPM) for sinal preprocessing, Performence Monotoring Module(PMM) for subsystem performance monitoring, Trigger Module(TM) for multi-triggering subsystem fault diagnosis, Subsystem Fault Diagnosis Agent(SFDA) for receiving trigger signal, formulating subsystem fault D\ulcornerB and initiating diagnosis, Fault Diagnosis Module(FDM) for simulating component fault with Hierarchical Artificial Neural Network (HANN), numerical models and Hofield network,Result Agent(RA) for receiving simulation result and sending to Treatment solver and Graphic Agent(GA). Each agent represents a separate process in UNIX operating system, information exchange and cooperation between agents was doen by IPC(Inter Process Communication : message queue, semaphore, signal, pipe). Numerical models are used to deseribe structure, function and behavior of total system, subsystems and their components. Hierarchical data structure for diagnosing the fault system is implemented by HANN. Signal generation and transmittion was performed on PC. As a host, SUN workstation with X-Windows(Motif)is used for graphic representation.

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Some Worthy Signal Processing Techniques for Mechanical Fault Diagnosis

  • Chan, Jin
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.39-52
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    • 2002
  • Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points: Fault feature extracting techniques based on (modern) signal processing(omitted)

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Fault diagnosis based on likelihood decomposition

  • Uosaki, Katsuji;Kagawa, Tetsuo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.272-275
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    • 1992
  • A novel fault diagnosis method based on likelihood decomposition is proposed for linear stochastic systems described by autoregressive (AR) model. Assuming that at some time instant .tau. the fault of one of the following two types is occurs: innovation fault (actuator fault); and observation fault (sensor fault), the log-likelihood function is decomposed into two components based on the observations before and after .tau., respectively, Then, the type of the fault is determined by comparing the log-likelihoods corresponding two types of faults. Numerical examples demonstrate the usefulness of the proposed diagnosis method.

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A New Study on Vibration Data Acquisition and Intelligent Fault Diagnostic System for Aero-engine

  • Ding, Yongshan;Jiang, Dongxiang
    • Proceedings of the Korean Society of Propulsion Engineers Conference
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    • pp.16-21
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    • 2008
  • Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

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A Fault Detection and Diagnosis in a PWR Steam Generator (PWM 증기발생기의 고장검출 및 진단에 관한 연구)

  • Park, Seung-Yub
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.40 no.1
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    • pp.120-127
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    • 1991
  • The purpose of this study is to develop a fault detection and diagnosis scheme that can monitor process fault and instrument fault of a steam generator. The suggested scheme consists of a Kalman filter and two bias estimators. Method of detecting process and instrument fault in a steam generator uses the mean test on the residual sequence of Kalman filter, designed for the unfailed system, to make a fault decision. Once a fault is detected, two bias estimators are driven to estimate the fault and to discriminate process fault and instrument fault. In case of process fault, the fault diagnosis of outlet temperature, feed-water heater and main steam control value is considered. In instrument fault, the fault diagnosis of steam genrator's three instruments is considered. Computer simulation tests show that on-line prompt fault detection and diagnosis can be performed very successfully.

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Implementation of an 1/O Card Fault Diagnosis System In Power Plant Simulator (발전소 사뮬레이터 I/O 카드 레벨 고장 진단 시스템의 구현)

  • Byun, S.H.;Ma, B.R.
    • Proceedings of the KIEE Conference
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    • pp.3192-3194
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    • 2000
  • Many I/o cards such as AOCs, DICs, DOCs and ROCs are used to deal with I&C instruments of control panel in full-scope power plant simulator. To help the maintenance of I/O cards, an I/o card fault diagnosis system is implemented in this paper. The implemented fault diagnosis system has the automatic fault diagnosis function and manual card test function for fault diagnosis. Finally, the test result using I/O cards shows the validity of the implemented fault diagnosis system.

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The Development of Diesel Engine Room Fault Diagnosis System Using a Correlation Analysis Method (상관분석법에 의한 선박기관실 고장진단 시스템 개발)

  • Kim, Young-Il;Oh, Hyun-Kyung;Yu, Yung-Ho
    • Journal of the Korean Society of Marine Engineering
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    • v.30 no.2
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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.