• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.444-451
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• 1999

A diagnostic rule-base design method for enhancing fault detection and isolation performance of multiple obsever based fault detection isolation schemes (FIDS) is presented. The diagnostic rule-base has a hierarchical framework to perform detection and isolation of faults of interest, and diagnosis of process faults. The decision unit comprises a rule base and a fuzzy inference engine and removes some difficulties of conventional decision unit which includes crisp logic with threshold values. Emphasis is placed on the design and evaluation methods of the diagnostic rult-base. The suggested scheme is applied to the FDIS design for a DC motor driven centrifugal pump system.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.307-312
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• 2020

This paper proposed intelligent motor control system that replaced smart motor devices, such as motor protection relays, smart circuit breakers and variable speed drives, with one integrated module to perform efficient motor control at industrial sites. The proposed intelligent motor control system provides easy monitoring of critical data for each motor or load connected to an intelligent motor control system over a CIP(Common Industrial Protocol)-based network, which enables accurate process control at all times, real-time access to fault information and records to simplify diagnosis and minimize equipment downtime.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.2
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• pp.152-159
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• 2005

In this paper, we propose a diagnosis algorithm using fusion wかd based on PCA and LDA to detect fault states of the induction motor that is applied to various industrial fields. After yielding a feature vector from the current value measured by an experiment using PCA and LDA, training data is made to produce each matching value. In a diagnostic step, two matching values yielded by PCA and LDA are fused by probability model and finally verified. Since the proposed diagnosis algorithm takes only merits of PCA and LDA it shows excellent results under noisy environments. The simulation results to verify the usability of the proposed algorithm showed better performance than the case just using conventional PCA or LDA.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.55-61
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• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.305-307
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• 2006

기존에 사용되어 온 진동데이터를 이용한 유도전동기 고장진단 기법은 유도전동기의 전기적 결함을 파악하기 어렵고 특정 고장의 경우 유사한 진동주파수를 포함하고 있어 정확한 고장진단이 어렵다. 본 논문에서는 유도전동기 고장진단 시스템을 구현하기 위해 기존의 진동데이터 분석에 전류 분석기법 중의 하나인 MCSA(Motor Current Signature Analysis)기법을 추가하여 유도전동기 예지보전시스템의 신뢰성을 향상시켰다. 구현된 시스템의 신뢰성을 검증하기 위해 유도전동기의 고장진단을 위한 실험환경을 구축하고 진동데이터만을 이용하여 얻어진 고장진단 결과와 전류데이터 분석을 병행하여 얻어진 고장진단 결과를 비교 분석하였다.

• Journal of Power Electronics
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• v.11 no.2
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• pp.163-172
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• 2011

Oxidation models of a rotor bar and end ring segment in an induction motor are presented to simulate the behavior of an induction machine working with oxidized rotor parts which are modeled as rotor faults in progress. The leakage inductance and resistance of the rotor parts arc different from normal values because of the oxidation process. The impedance variations modify the current density and magnetic flux which pass through the oxidized parts. Consequently, it causes the rotor asymmetry which induces abnormal harmonics in the stator current spectra of the faulty machine. The leakage inductances of the oxidation models are derived by the Ampere's law. Using the proposed oxidation models, the rotor bar and end ring faults in progress can be modeled and simulated with the motor current signature analysis (MCSA). In addition, the oxidation process of the rotor bar and end ring segment can motivate the rotor asymmetry, which is induced by electromagnetic imbalances, and it is one of the major motor faults. Results of simulations and experiments are compared to each other to verify the accuracy of the proposed models. Experiments are achieved using 3.7 kW, 3-phase, and squirrel cage induction motors with a motor drive inverter.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.410-413
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• 1998

A passive approach for enhancing fault detection and isolation performance of multiple observer based fault detection isolation schemes(FDIS) is proposed. The FDIS has a hierarchical framework to perform detection and isolation of faults of interest, and diagnosis of process faults. The decision unit comprises of a rule base and fuzzy inference engine and removes some difficulties of conventional decision unit which includes crisp logic and threshold values. Emphasis is placed on the design and evaluation methods of the diagnostic rule base. The suggested scheme is applied for the FDIS design for a DC motor driven centrifugal pump system.

• Journal of IKEEE
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• v.28 no.2
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• pp.180-186
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• 2024

This paper proposes an efficient fault diagnosis method for ITSC(Inter-Turn Short Circuit) in three-phase induction motors using CNN. By utilizing only the D-axis component of the D-Q synchronous coordinate system, it compares SWM(Slide Window Method) and GAF(Gramian Angular Field) methods for image encoding. Results show GAF achieving ~74% accuracy, while SWM achieves ~65%, indicating GAF's superiority by 9%. Learning time (~14.74s) remains consistent, particularly with epochs ≤ 100, showcasing faster learning.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.