• 전력전자학회:학술대회논문집
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• 전력전자학회 2007년도 하계학술대회 논문집
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• pp.325-327
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• 2007

This paper proposes a novel sensorless fault detection algorithm for a brushless DC(BLDC) motor drive system. This proposed method is configured without the additional sensor for fault detection and identification. The fault detection and identification are achieved by a simple algorithm using the operating characteristic of the BLDC motor. This proposed method can also be embedded into existing BLDC motor drive systems as a subroutine without excessive computational effort. The feasibility of a novel sensorless fault detection algorithm is validated in simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.7-12
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• 1994

This paper describes an outline of pseudorandom M-sequence and its applications to measurement and control engineering. At first, generation and properties of M-sequence is briefly described and then its applications to delay time measurement, information transmission by use of M-array, two dimensional positioning, fault detection of logical circuit, fault detection of RAM, linear and nonlinear system identification.

• 대한전기학회논문지:전력기술부문A
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• 제48권4호
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• 전력전자학회논문지
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• 제13권2호
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• pp.88-95
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• 2008

본 논문에서는 BLDC 전동기 구동용 인버터 한상의 스위치에서 개방 형태의 고장이 발생하여도 구동 시스템의 제어 성능을 유지하기 위한 빠른 고장 검출 시스템이 제안되었다. 제안된 방법은 고장 검출을 위한 추가적인 구성요소 없이 단지 BLDC 전동기의 구동 시에 나타나는 고유의 전류 제어 특성을 이용하여 간단한 방식으로 고장 검출 및 고장 위치 확인이 가능하다. 신속히 고장이 발생한 스위치의 위치를 판단한 후, 시스템은 연속적인 동작을 위해 재구성되어진다. 재구성 방법은 고장이 발생한 상을 양방향 스위치에 의해 직류-링크 중성점과 연결하여 4-스위치 구동의 형태로 구성하였다. 제안한 고장 검출 시스템은 짧은 고장 검출 시간과 시스템 토폴로지의 재구성에 의해 고장 발생 후 제어 성능을 빠르게 회복하여 연속적인 시스템 운영이 가능하다. 제안한 고장 검출 시스템의 우수한 성능은 시뮬레이션과 실험을 통하여 검증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권4호
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• pp.238-245
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• 2016

Inverters are considered the basic building blocks of industrial electrical drive systems that are widely used for various applications; however, the failure of electronic switches mainly affects the constancy of these inverters. For safe and reliable operation of an electrical drive system, faults in power electronic switches must be detected by an efficient system that is capable of identifying the type of faults. In this paper, an open switch fault identification technique for a three-phase inverter is presented. Single, double, and triple switching faults can be diagnosed using this method. The detection mechanism is based on stator current analysis. Discrete wavelet transform (DWT) using Daubechies is performed on the Clarke transformed (-) stator current and features are extracted from the wavelets. An artificial neural network is then used for the detection and identification of faults. To prove the feasibility of this method, a Simulink model of the DWT-based feature extraction scheme using a neural network for the proposed fault detection system in a three-phase inverter with an induction motor is briefly discussed with simulation results. The simulation results show that the designed system can detect faults quite efficiently, with the ability to differentiate between single and multiple switching faults.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.341-344
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• 1997

This paper presents a dynamics failure detection algorithm developed for the two-motor-driven electric vehicle system. The algorithm is based on the application of the fault detection filter. The fault detection includes the identification of sudden pressure drops of the two rear tires in driving axis and dynamics faults of the two inverter-motor-paired actuators An E.V. dynamics simulator is developed, which includes the modeling of the E.V. dynamics as well as the driving dynamics. The simulator, which allows the generation of various fault situations, is utilized in the verification of the developed fault detection algorithm. The results of the simulations are also presented.

• 한국통신학회논문지
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• 제17권11호
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• pp.1279-1289
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• 1992

고장 방지 시스템은 하드웨어나 소프트웨어의 여분 (Redundancy)을 이용하여 신뢰도(Reliability) 및 안전도(Safety)를 향상 시킨다. 시스템의 대상 영역(application areas)에 따라 고장 마스크(fault mask), 고장검출(fault detection), 고장 확인(fault identification)등의 기법을 선택하여 이용한다. 본 연구에서는 최소의 하드웨어와 소프트웨어의 여분을 이용하는 DMR(Double Modular Redundancy) 시스템을 대기 모듈(standby module)과 Fail-safe 모듈로 동작 시킬때 신뢰도와 안전도의 특성을 비교 제시한다. 또한 자기 진단 프로그램의 과도 오류 방지 능력에 대한 시스템의 MTTF를 비교함으로서 과도 오류를 취급하는 효과적인 방법을 제시하였다.

• Journal of Power Electronics
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• 제16권3호
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.136-144
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• 1999

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.